Updated on 2026/04/03

写真a

 
SATO KENGO
 
Organization
School of Life Science and Technology Professor
Title
Professor
Homepage
https://www.sato-lab.org/en/
External link

Degree

  • Ph.D. in Engineering ( 2003.3   Keio University )

  • Master of Science in Engineering ( 1997.3   Keio University )

  • Bachelor of Science ( 1995.3   Keio University )

Research Interests

  • Biomedical data science

  • Bioinformatics

  • Sequence Analysis

  • Machine learning

  • Computational Biology

Research Areas

  • Informatics / Intelligent informatics  / Intelligent Informatics

  • Informatics / Life, health and medical informatics

  • Life Science / System genome science

Education

  • Keio University   Graduate School of Science and Technology   School of Science for Open and Environmental Systems

    1997.4 - 2003.3

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    Country： Japan

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  • Keio University   Graduate School of Science and Technology   Department of Computer Science

    1995.4 - 1997.3

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    Country： Japan

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  • Keio University   Faculty of Science and Technology   Department of Mathematics

    1991.4 - 1995.3

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    Country： Japan

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Research History

  • Institute of Science Tokyo   School of Life Science and Technology   Professor

    2024.10

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  • Tokyo Institute of Technology   School of Life Science and Technology   Professor

    2024.8 - 2024.9

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    Country：Japan

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  • Tokyo Denki University   School of System Design and Technology Department of Information System Engineering   Professor

    2022.4 - 2024.7

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    Country：Japan

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  • Keio University   Department of Biosciences and Informatics, Faculty of Science and Technology   Lecturer

    2011.4 - 2022.3

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  • The University of Tokyo   Department of Computational Biology, Graduate School of Frontier Sciences   Project Assistant Professor

    2009.11 - 2011.3

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  • National Institute of Advanced Industrial Science and Technology   Computational Biology Research Center   Visiting Researcher

    2006.4 - 2015.3

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  • Japan Biological Informatics Consortium   Researcher

    2006.4 - 2009.10

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  • Keio University   Department of Biosciences and Informatics, Faculty of Science and Technology   Research Associate

    2003.4 - 2006.3

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Professional Memberships

  • Japan Society of Marmoset Research

    2013 - 2021

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  • The RNA Society of Japan

    2011

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  • 日本分子生物学会

    2008

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  • IPSJ SIGBIO

    2006

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  • International Society for Computational Biology

    2005

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  • Japanese Society for Bioinformatics

    2004

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  • Information Processing Society of Japan

    1995

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  • 言語処理学会

    1995 - 2012

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Committee Memberships

  • 情報処理学会バイオ情報学研究会   主査  

    2023.4   

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    Committee type：Academic society

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  • 情報処理学会バイオ情報学研究会   幹事  

    2022.4 - 2023.3   

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    Committee type：Academic society

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  • 第20回情報科学技術フォーラム (FIT 2021)   研究専門委員会担当委員  

    2021   

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    Committee type：Academic society

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  • 日本バイオインフォマティクス学会   理事  

    2020.4 - 2024.3   

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    Committee type：Academic society

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  • 情報処理学会バイオ情報学研究会   運営委員  

    2018.4 - 2022.3   

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    Committee type：Academic society

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  • Asia Pacific Bioinformatics Conference (APBC) 2018   Local Organizing Committee Co-Chair  

    2018   

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    Committee type：Academic society

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  • Genome Informatics Workshop (GIW) 2018   Program Committee Co-chair  

    2018   

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    Committee type：Academic society

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  • 2016年度生命医薬情報学連合大会 (IIBMP)   実行委員  

    2016   

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    Committee type：Academic society

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  • 情報処理学会バイオ情報学研究会   運営委員  

    2013.4 - 2017.3   

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    Committee type：Academic society

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Papers

  • Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery Reviewed International journal

    Kengo Sato, Michiaki Hamada

    Briefings in Bioinformatics   24 ( 4 )   2023.5

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

    Abstract

    Computational analysis of RNA sequences constitutes a crucial step in the field of RNA biology. As in other domains of the life sciences, the incorporation of artificial intelligence and machine learning techniques into RNA sequence analysis has gained significant traction in recent years. Historically, thermodynamics-based methods were widely employed for the prediction of RNA secondary structures; however, machine learning-based approaches have demonstrated remarkable advancements in recent years, enabling more accurate predictions. Consequently, the precision of sequence analysis pertaining to RNA secondary structures, such as RNA–protein interactions, has also been enhanced, making a substantial contribution to the field of RNA biology. Additionally, artificial intelligence and machine learning are also introducing technical innovations in the analysis of RNA–small molecule interactions for RNA-targeted drug discovery and in the design of RNA aptamers, where RNA serves as its own ligand. This review will highlight recent trends in the prediction of RNA secondary structure, RNA aptamers and RNA drug discovery using machine learning, deep learning and related technologies, and will also discuss potential future avenues in the field of RNA informatics.

    DOI： 10.1093/bib/bbad186

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  • Prediction of RNA secondary structure including pseudoknots for long sequences, Reviewed International journal

    Kengo Sato, Yuki Kato

    Briefings in Bioinformatics   23 ( 1 )   2022.1

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

    <title>Abstract</title>
    RNA structural elements called pseudoknots are involved in various biological phenomena including ribosomal frameshifts. Because it is infeasible to construct an efficiently computable secondary structure model including pseudoknots, secondary structure prediction methods considering pseudoknots are not yet widely available. We developed IPknot, which uses heuristics to speed up computations, but it has remained difficult to apply it to long sequences, such as messenger RNA and viral RNA, because it requires cubic computational time with respect to sequence length and has threshold parameters that need to be manually adjusted. Here, we propose an improvement of IPknot that enables calculation in linear time by employing the LinearPartition model and automatically selects the optimal threshold parameters based on the pseudo-expected accuracy. In addition, IPknot showed favorable prediction accuracy across a wide range of conditions in our exhaustive benchmarking, not only for single sequences but also for multiple alignments.

    DOI： 10.1093/bib/bbab395

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  • RNA secondary structure prediction using deep learning with thermodynamic integration Reviewed International journal

    Kengo Sato, Manato Akiyama, Yasubumi Sakakibara

    Nature Communications   12 ( 1 )   2021.2

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

    Abstract

    Accurate predictions of RNA secondary structures can help uncover the roles of functional non-coding RNAs. Although machine learning-based models have achieved high performance in terms of prediction accuracy, overfitting is a common risk for such highly parameterized models. Here we show that overfitting can be minimized when RNA folding scores learnt using a deep neural network are integrated together with Turner’s nearest-neighbor free energy parameters. Training the model with thermodynamic regularization ensures that folding scores and the calculated free energy are as close as possible. In computational experiments designed for newly discovered non-coding RNAs, our algorithm (MXfold2) achieves the most robust and accurate predictions of RNA secondary structures without sacrificing computational efficiency compared to several other algorithms. The results suggest that integrating thermodynamic information could help improve the robustness of deep learning-based predictions of RNA secondary structure.

    DOI： 10.1038/s41467-021-21194-4

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    Other Link： https://www.nature.com/articles/s41467-021-21194-4

  • IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming Reviewed International journal

    Kengo Sato, Yuki Kato, Michiaki Hamada, Tatsuya Akutsu, Kiyoshi Asai

    Bioinformatics   27 ( 13 )   i85 - i93   2011.6

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

    DOI： 10.1093/bioinformatics/btr215

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  • Blind Prediction of Complex Water and Ion Ensembles Around <scp>RNA</scp> in <scp>CASP16</scp>

    Rachael C. Kretsch, Elisa Posani, Eugene F. Baulin, Janusz M. Bujnicki, Giovanni Bussi, Thomas E. Cheatham, Shi‐Jie Chen, Arne Elofsson, Masoud Amiri Farsani, Olivia N. Fisher, M. Michael Gromiha, Ayush Gupta, Michiaki Hamada, K. Harini, Gang Hu, David Huang, Junichi Iwakiri, Anika Jain, Yuki Kagaya, Daisuke Kihara, Sebastian Kmiecik, Sowmya Ramaswamy Krishnan, Ikuo Kurisaki, Olivier Languin‐Cattoën, Jun Li, Shanshan Li, Karim Malekzadeh, Tsukasa Nakamura, Wentao Ni, Chandran Nithin, Michael Z. Palo, Joon Hong Park, Smita P. Pilla, Simón Poblete, Fabrizio Pucci, Pranav Punuru, Anouka Saha, Kengo Sato, Ambuj Srivastava, Genki Terashi, Emilia Tugolukova, Jacob Verburgt, Qiqige Wuyun, Gül H. Zerze, Kaiming Zhang, Sicheng Zhang, Wei Zheng, Yuanzhe Zhou, Wah Chiu, David A. Case, Rhiju Das

    Proteins: Structure, Function, and Bioinformatics   2025.11

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    Publishing type：Research paper (scientific journal)   Publisher：Wiley  

    ABSTRACT

    Biomolecules rely on water and ions for stable folding, but these interactions are often transient, dynamic, or disordered and thus hidden from experiments and evaluation challenges that represent biomolecules as single, ordered structures. Here, we compare blindly predicted ensembles of water and ion structure to the cryo‐EM densities observed around the Tetrahymena ribozyme at 2.2–2.3 Å resolution, collected through target R1260 in the CASP16 competition. Twenty‐six groups participated in this solvation “cryo‐ensemble” prediction challenge, submitting over 350 million atoms in total, offering the first opportunity to compare blind predictions of dynamic solvent shell ensembles to cryo‐EM density. Predicted atomic ensembles were converted to density through local alignment and these densities were compared to the cryo‐EM densities using Pearson correlation, Spearman correlation, mutual information, and precision‐recall curves. These predictions show that an ensemble representation is able to capture information of transient or dynamic water and ions better than traditional atomic models, but there remains a large accuracy gap to the performance ceiling set by experimental uncertainty. Overall, molecular dynamics approaches best matched the cryo‐EM density, with blind predictions from bussilab_plain_md, SoutheRNA, bussilab_replex, coogs2, and coogs3 outperforming the baseline molecular dynamics prediction. This study indicates that simulations of water and ions can be quantitatively evaluated with cryo‐EM maps. We propose that further community‐wide blind challenges can drive and evaluate progress in modeling water, ions, and other previously hidden components of biomolecular systems.

    DOI： 10.1002/prot.70079

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  • RNA secondary structure prediction by conducting multi-class classifications Reviewed International journal

    Jiyuan Yang, Kengo Sato, Martin Loza, Sung-Joon Park, Kenta Nakai

    Computational and Structural Biotechnology Journal   2025.4

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1016/j.csbj.2025.04.001

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  • Cell type–specific functions of nucleic acid-binding proteins revealed by deep learning on co-expression networks International journal

    Naoki Osato, Kengo Sato

    2025.3

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    Authorship：Last author   Language：English   Publisher：Cold Spring Harbor Laboratory  

    Abstract

    Nucleic acid-binding proteins (NABPs) exhibit cell type–specific regulatory functions, but their target genes and biological roles remain incompletely characterized due to the limitations of current experimental approaches. Here, we present a deep learning framework that integrates gene co-expression correlations to predict NABP regulatory targets and infer their functions across diverse cellular contexts, without requiring binding site or motif information. Substituting low-informative input features with co-expression-derived interactions improved gene expression prediction accuracy. Predicted targets showed strong concordance with ChIP-seq and eCLIP binding sites, and this agreement was significantly greater than for randomly selected gene sets. Functional enrichment and ChatGPT-assisted inference revealed biologically meaningful annotations, including cell type–specific functions such as circadian regulation by AKAP8 in cancer cells and glycolytic control by PKM. Collectively, this integrative framework—combining deep learning, co-expression networks, and large language models—enables the systematic discovery of both known and previously uncharacterized NABP functions in a cell type–specific manner.

    DOI： 10.1101/2025.03.03.641203

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  • A functional connection between the Microprocessor and a variant NEXT complex Reviewed International coauthorship International journal

    Katsutoshi Imamura, William Garland, Manfred Schmid, Lis Jakobsen, Kengo Sato, Jérôme O. Rouvière, Kristoffer Pors Jakobsen, Elena Burlacu, Marta Loureiro Lopez, Søren Lykke-Andersen, Jens S. Andersen, Torben Heick Jensen

    Molecular Cell   84 ( 21 )   4158 - 4174.e6   2024.11

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

    DOI： 10.1016/j.molcel.2024.10.015

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  • Direct Inference of Base-Pairing Probabilities with Neural Networks Improves Prediction of RNA Secondary Structures with Pseudoknots Reviewed International journal

    Manato Akiyama, Yasubumi Sakakibara, Kengo Sato

    Genes   13 ( 11 )   2155   2022.11

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    Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：MDPI  

    DOI： 10.3390/genes13112155

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  • Integer programming for selecting set of informative markers in paternity inference, Reviewed International journal

    Soichiro Nishiyama, Kengo Sato, Ryutaro Tao

    BMC Bioinformatics   23 ( 1 )   265 - 265   2022.7

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    Language：English   Publishing type：Research paper (scientific journal)  

    BACKGROUND: Parentage information is fundamental to various life sciences. Recent advances in sequencing technologies have made it possible to accurately infer parentage even in non-model species. The optimization of sets of genome-wide markers is valuable for cost-effective applications but requires extremely large amounts of computation, which presses for the development of new efficient algorithms. RESULTS: Here, for a closed half-sib population, we generalized the process of marker loci selection as a binary integer programming problem. The proposed systematic formulation considered marker localization and the family structure of the potential parental population, resulting in an accurate assignment with a small set of markers. We also proposed an efficient heuristic approach, which effectively improved the number of markers, localization, and tolerance to missing data of the set. Applying this method to the actual genotypes of apple (Malus × domestica) germplasm, we identified a set of 34 SNP markers that distinguished 300 potential parents crossed to a particular cultivar with a greater than 99% accuracy. CONCLUSIONS: We present a novel approach for selecting informative markers based on binary integer programming. Since the data generated by high-throughput sequencing technology far exceeds the requirement for parentage assignment, a combination of the systematic marker selection with targeted SNP genotyping, such as KASP, allows flexibly enlarging the analysis up to a scale that has been unrealistic in various species. The method developed in this study can be directly applied to unsolved large-scale problems in breeding, reproduction, and ecological research, and is expected to lead to novel knowledge in various biological fields. The implementation is available at https://github.com/SoNishiyama/IP-SIMPAT .

    DOI： 10.1186/s12859-022-04801-z

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  • A Max-Margin Model for Predicting Residue—Base Contacts in Protein–RNA Interactions Reviewed International journal

    Shunya Kashiwagi, Kengo Sato, Yasubumi Sakakibara

    Life   11 ( 11 )   1135   2021.10

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    Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：MDPI  

    DOI： 10.3390/life11111135

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  • A Web Server for Designing Molecular Switches Composed of Two Interacting RNAs Reviewed International journal

    Akito Taneda, Kengo Sato

    International Journal of Molecular Sciences   22 ( 5 )   2720   2021.3

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    Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：MDPI  

    DOI： 10.3390/ijms22052720

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  • An improved de novo genome assembly of the common marmoset genome yields improved contiguity and increased mapping rates of sequence data Reviewed International journal

    Vasanthan Jayakumar, Hiromi Ishii, Misato Seki, Wakako Kumita, Takashi Inoue, Sumitaka Hase, Kengo Sato, Hideyuki Okano, Erika Sasaki, Yasubumi Sakakibara

    BMC Genomics   21 ( S3 )   2020.4

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

    Abstract

    Background

    The common marmoset (Callithrix jacchus) is one of the most studied primate model organisms. However, the marmoset genomes available in the public databases are highly fragmented and filled with sequence gaps, hindering research advances related to marmoset genomics and transcriptomics.

    Results

    Here we utilize single-molecule, long-read sequence data to improve and update the existing genome assembly and report a near-complete genome of the common marmoset. The assembly is of 2.79 Gb size, with a contig N50 length of 6.37 Mb and a chromosomal scaffold N50 length of 143.91 Mb, representing the most contiguous and high-quality marmoset genome up to date. Approximately 90% of the assembled genome was represented in contigs longer than 1 Mb, with approximately 104-fold improvement in contiguity over the previously published marmoset genome. More than 98% of the gaps from the previously published genomes were filled successfully, which improved the mapping rates of genomic and transcriptomic data on to the assembled genome.

    Conclusions

    Altogether the updated, high-quality common marmoset genome assembly provide improvements at various levels over the previous versions of the marmoset genome assemblies. This will allow researchers working on primate genomics to apply the genome more efficiently for their genomic and transcriptomic sequence data.

    DOI： 10.1186/s12864-020-6657-2

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    Other Link： http://link.springer.com/article/10.1186/s12864-020-6657-2/fulltext.html

  • Efficient generation of Knock-in/Knock-out marmoset embryo via CRISPR/Cas9 gene editing Reviewed International coauthorship

    Wakako Kumita, Kenya Sato, Yasuhiro Suzuki, Yoko Kurotaki, Takeshi Harada, Yang Zhou, Noriyuki Kishi, Kengo Sato, Atsu Aiba, Yasubumi Sakakibara, Guoping Feng, Hideyuki Okano, Erika Sasaki

    Scientific Reports   9 ( 1 )   2019.9

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    Abstract

    Genetically modified nonhuman primates (NHP) are useful models for biomedical research. Gene editing technologies have enabled production of target-gene knock-out (KO) NHP models. Target-gene-KO/knock-in (KI) efficiency of CRISPR/Cas9 has not been extensively investigated in marmosets. In this study, optimum conditions for target gene modification efficacies of CRISPR/mRNA and CRISPR/nuclease in marmoset embryos were examined. CRISPR/nuclease was more effective than CRISPR/mRNA in avoiding mosaic genetic alteration. Furthermore, optimal conditions to generate KI marmoset embryos were investigated using CRISPR/Cas9 and 2 different lengths (36 nt and 100 nt) each of a sense or anti-sense single-strand oligonucleotide (ssODN). KIs were observed when CRISPR/nuclease and 36 nt sense or anti-sense ssODNs were injected into embryos. All embryos exhibited mosaic mutations with KI and KO, or imprecise KI, of c-kit. Although further improvement of KI strategies is required, these results indicated that CRISPR/Cas9 may be utilized to produce KO/KI marmosets via gene editing.

    DOI： 10.1038/s41598-019-49110-3

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    Other Link： https://www.nature.com/articles/s41598-019-49110-3

  • Convolutional neural network based on SMILES representation of compounds for detecting chemical motif Reviewed International journal

    Maya Hirohara, Yutaka Saito, Yuki Koda, Kengo Sato, Yasubumi Sakakibara

    BMC Bioinformatics   19 ( S19 )   2018.12

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

    DOI： 10.1186/s12859-018-2523-5

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    Other Link： http://link.springer.com/article/10.1186/s12859-018-2523-5/fulltext.html

  • A max-margin training of RNA secondary structure prediction integrated with the thermodynamic model Reviewed International journal

    Manato Akiyama, Kengo Sato, Yasubumi Sakakibara

    Journal of Bioinformatics and Computational Biology   16 ( 06 )   1840025 - 1840025   2018.12

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    Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：World Scientific Pub Co Pte Ltd  

    A popular approach for predicting RNA secondary structure is the thermodynamic nearest-neighbor model that finds a thermodynamically most stable secondary structure with minimum free energy (MFE). For further improvement, an alternative approach that is based on machine learning techniques has been developed. The machine learning-based approach can employ a fine-grained model that includes much richer feature representations with the ability to fit the training data. Although a machine learning-based fine-grained model achieved extremely high performance in prediction accuracy, a possibility of the risk of overfitting for such a model has been reported. In this paper, we propose a novel algorithm for RNA secondary structure prediction that integrates the thermodynamic approach and the machine learning-based weighted approach. Our fine-grained model combines the experimentally determined thermodynamic parameters with a large number of scoring parameters for detailed contexts of features that are trained by the structured support vector machine (SSVM) with the [Formula: see text] regularization to avoid overfitting. Our benchmark shows that our algorithm achieves the best prediction accuracy compared with existing methods, and heavy overfitting cannot be observed. The implementation of our algorithm is available at https://github.com/keio-bioinformatics/mxfold .

    DOI： 10.1142/s0219720018400255

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  • Extension of Question-Answering Program to Automatically Answering the Medical Licensing Examination to Drug Related Questions Reviewed

    Transactions of the Japanese Society for Artificial Intelligence   33 ( 6 )   E-I58_1-10   2018.11

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    Language：Japanese   Publishing type：Research paper (scientific journal)  

    DOI： 10.1527/tjsai.E-I58

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  • DEclust: A statistical approach for obtaining differential expression profiles of multiple conditions Reviewed International journal

    Yoshimasa Aoto, Tsuyoshi Hachiya, Kazuhiro Okumura, Sumitaka Hase, Kengo Sato, Yuichi Wakabayashi, Yasubumi Sakakibara

    PLOS ONE   12 ( 11 )   e0188285 - e0188285   2017.11

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Public Library of Science (PLoS)  

    DOI： 10.1371/journal.pone.0188285

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  • An accessibility-incorporated method for accurate prediction of RNA–RNA interactions from sequence data Reviewed International journal

    Yuki Kato, Tomoya Mori, Kengo Sato, Shingo Maegawa, Hiroshi Hosokawa, Tatsuya Akutsu

    Bioinformatics   33 ( 2 )   202 - 209   2016.9

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

    Abstract

    Motivation

    RNA–RNA interactions via base pairing play a vital role in the post-transcriptional regulation of gene expression. Efficient identification of targets for such regulatory RNAs needs not only discriminative power for positive and negative RNA–RNA interacting sequence data but also accurate prediction of interaction sites from positive data. Recently, a few studies have incorporated interaction site accessibility into their prediction methods, indicating the enhancement of predictive performance on limited positive data.

    Results

    Here we show the efficacy of our accessibility-based prediction model RactIPAce on newly compiled datasets. The first experiment in interaction site prediction shows that RactIPAce achieves the best predictive performance on the newly compiled dataset of experimentally verified interactions in the literature as compared with the state-of-the-art methods. In addition, the second experiment in discrimination between positive and negative interacting pairs reveals that the combination of accessibility-based methods including our approach can be effective to discern real interacting RNAs. Taking these into account, our prediction model can be effective to predict interaction sites after screening for real interacting RNAs, which will boost the functional analysis of regulatory RNAs.

    Availability and Implementation

    The program RactIPAce along with data used in this work is available at https://github.com/satoken/ractip/releases/tag/v1.0.1.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

    DOI： 10.1093/bioinformatics/btw603

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  • Generation of a Nonhuman Primate Model of Severe Combined Immunodeficiency Using Highly Efficient Genome Editing Reviewed International coauthorship International journal

    Kenya Sato, Ryo Oiwa, Wakako Kumita, Rachel Henry, Tetsushi Sakuma, Ryoji Ito, Ryoko Nozu, Takashi Inoue, Ikumi Katano, Kengo Sato, Norio Okahara, Junko Okahara, Yoshihisa Shimizu, Masafumi Yamamoto, Kisaburo Hanazawa, Takao Kawakami, Yoshie Kametani, Ryuji Suzuki, Takeshi Takahashi, Edward J. Weinstein, Takashi Yamamoto, Yasubumi Sakakibara, Sonoko Habu, Jun-ichi Hata, Hideyuki Okano, Erika Sasaki

    Cell Stem Cell   19 ( 1 )   127 - 138   2016.7

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

    DOI： 10.1016/j.stem.2016.06.003

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  • SHARAKU: an algorithm for aligning and clustering read mapping profiles of deep sequencing in non-coding RNA processing Reviewed International journal

    Mariko Tsuchiya, Kojiro Amano, Masaya Abe, Misato Seki, Sumitaka Hase, Kengo Sato, Yasubumi Sakakibara

    Bioinformatics   32 ( 12 )   i369 - i377   2016.6

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

    DOI： 10.1093/bioinformatics/btw273

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  • Rtools: a web server for various secondary structural analyses on single RNA sequences Reviewed International journal

    Michiaki Hamada, Yukiteru Ono, Hisanori Kiryu, Kengo Sato, Yuki Kato, Tsukasa Fukunaga, Ryota Mori, Kiyoshi Asai

    Nucleic Acids Research   44 ( W1 )   W302 - W307   2016.4

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

    DOI： 10.1093/nar/gkw337

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  • Prediction of Gene Structures from RNA-seq Data Using Dual Decomposition Reviewed International journal

    Tatsumu Inatsuki, Kengo Sato, Yasubumi Sakakibara

    IPSJ Transactions on Bioinformatics   9   1 - 6   2016

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    Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Information Processing Society of Japan  

    DOI： 10.2197/ipsjtbio.9.1

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  • Resequencing of the common marmoset genome improves genome assemblies and gene-coding sequence analysis Reviewed International journal

    Kengo Sato, Yoko Kuroki, Wakako Kumita, Asao Fujiyama, Atsushi Toyoda, Jun Kawai, Atsushi Iriki, Erika Sasaki, Hideyuki Okano, Yasubumi Sakakibara

    Scientific Reports   5 ( 1 )   2015.11

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    Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

    Abstract

    The first draft of the common marmoset (Callithrix jacchus) genome was published by the Marmoset Genome Sequencing and Analysis Consortium. The draft was based on whole-genome shotgun sequencing and the current assembly version is Callithrix_jacches-3.2.1, but there still exist 187,214 undetermined gap regions and supercontigs and relatively short contigs that are unmapped to chromosomes in the draft genome. We performed resequencing and assembly of the genome of common marmoset by deep sequencing with high-throughput sequencing technology. Several different sequence runs using Illumina sequencing platforms were executed and 181 Gbp of high-quality bases including mate-pairs with long insert lengths of 3, 8, 20 and 40 Kbp were obtained, that is, approximately 60× coverage. The resequencing significantly improved the MGSAC draft genome sequence. The N50 of the contigs, which is a statistical measure used to evaluate assembly quality, doubled. As a result, 51% of the contigs (total length: 299 Mbp) that were unmapped to chromosomes in the MGSAC draft were merged with chromosomal contigs and the improved genome sequence helped to detect 5,288 new genes that are homologous to human cDNAs and the gaps in 5,187 transcripts of the Ensembl gene annotations were completely filled.

    DOI： 10.1038/srep16894

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    Other Link： https://www.nature.com/articles/srep16894

  • A Machine Learning Based Approach to &lt;italic&gt;de novo&lt;/italic&gt; Sequencing of Glycans from Tandem Mass Spectrometry Spectrum Reviewed International journal

    Shotaro Kumozaki, Kengo Sato, Yasubumi Sakakibara

    IEEE/ACM Transactions on Computational Biology and Bioinformatics   12 ( 6 )   1267 - 1274   2015.11

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    Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Institute of Electrical and Electronics Engineers (IEEE)  

    DOI： 10.1109/tcbb.2015.2430317

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  • Whole-Genome Sequencing and Comparative Genome Analysis of Bacillus subtilis Strains Isolated from Non-Salted Fermented Soybean Foods Reviewed International journal

    Mayumi Kamada, Sumitaka Hase, Kazushi Fujii, Masato Miyake, Kengo Sato, Keitarou Kimura, Yasubumi Sakakibara

    PLOS ONE   10 ( 10 )   e0141369 - e0141369   2015.10

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    DOI： 10.1371/journal.pone.0141369

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  • MetaVelvet-SL: an extension of the Velvet assembler to a de novo metagenomic assembler utilizing supervised learning Reviewed International journal

    Afiahayati, K. Sato, Y. Sakakibara

    DNA Research   22 ( 1 )   69 - 77   2014.11

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    DOI： 10.1093/dnares/dsu041

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  • Whole Genome Complete Resequencing of Bacillus subtilis Natto by Combining Long Reads with High-Quality Short Reads Reviewed International journal

    Mayumi Kamada, Sumitaka Hase, Kengo Sato, Atsushi Toyoda, Asao Fujiyama, Yasubumi Sakakibara

    PLoS ONE   9 ( 10 )   e109999 - e109999   2014.10

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    DOI： 10.1371/journal.pone.0109999

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  • An extended genovo metagenomic assembler by incorporating paired-end information Reviewed International journal

    Afiahayati, Kengo Sato, Yasubumi Sakakibara

    PeerJ   1   e196 - e196   2013.10

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    DOI： 10.7717/peerj.196

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  • An efficient algorithm for de novo predictions of biochemical pathways between chemical compounds Reviewed International journal

    Masaomi Nakamura, Tsuyoshi Hachiya, Yutaka Saito, Kengo Sato, Yasubumi Sakakibara

    BMC Bioinformatics   13 ( S17 )   2012.12

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

    DOI： 10.1186/1471-2105-13-s17-s8

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    Other Link： http://link.springer.com/article/10.1186/1471-2105-13-S17-S8/fulltext.html

  • DAFS: simultaneous aligning and folding of RNA sequences via dual decomposition Reviewed International journal

    Kengo Sato, Yuki Kato, Tatsuya Akutsu, Kiyoshi Asai, Yasubumi Sakakibara

    Bioinformatics   28 ( 24 )   3218 - 3224   2012.10

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

    DOI： 10.1093/bioinformatics/bts612

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  • Rtips: fast and accurate tools for RNA 2D structure prediction using integer programming Reviewed International journal

    Y. Kato, K. Sato, K. Asai, T. Akutsu

    Nucleic Acids Research   40 ( W1 )   W29 - W34   2012.5

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

    DOI： 10.1093/nar/gks412

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  • Prediction of RNA Joint Secondary Structures Based on Integer Programming

    Yuki Kato, Kengo Sato

    Methods in Molecular Biology   99 - 107   2012.2

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    Publishing type：Part of collection (book)   Publisher：Springer US  

    DOI： 10.1007/978-1-0716-4670-0_5

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  • COPICAT: a software system for predicting interactions between proteins and chemical compounds Reviewed International journal

    Yasubumi Sakakibara, Tsuyoshi Hachiya, Miho Uchida, Nobuyoshi Nagamine, Yohei Sugawara, Masahiro Yokota, Masaomi Nakamura, Kris Popendorf, Takashi Komori, Kengo Sato

    Bioinformatics   28 ( 5 )   745 - 746   2012.1

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    Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

    DOI： 10.1093/bioinformatics/bts031

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  • USING BINDING PROFILES TO PREDICT BINDING SITES OF TARGET RNAs Reviewed International journal

    UNYANEE POOLSAP, YUKI KATO, KENGO SATO, TATSUYA AKUTSU

    Journal of Bioinformatics and Computational Biology   09 ( 06 )   697 - 713   2011.12

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：World Scientific Pub Co Pte Lt  

    DOI： 10.1142/s0219720011005628

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  • CentroidHomfold-LAST: accurate prediction of RNA secondary structure using automatically collected homologous sequences Reviewed International journal

    Michiaki Hamada, Koichiro Yamada, Kengo Sato, Martin C. Frith, Kiyoshi Asai

    Nucleic Acids Research   39 ( suppl_2 )   W100 - W106   2011.5

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

    DOI： 10.1093/nar/gkr290

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  • Fast and accurate clustering of noncoding RNAs using ensembles of sequence alignments and secondary structures Reviewed International journal

    Yutaka Saito, Kengo Sato, Yasubumi Sakakibara

    BMC Bioinformatics   12 ( S1 )   2011.2

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

    DOI： 10.1186/1471-2105-12-s1-s48

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  • Adaptive seeds tame genomic sequence comparison Reviewed International coauthorship International journal

    Szymon M. Kiełbasa, Raymond Wan, Kengo Sato, Paul Horton, Martin C. Frith

    Genome Research   21 ( 3 )   487 - 493   2011.1

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Cold Spring Harbor Laboratory  

    DOI： 10.1101/gr.113985.110

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  • Improved Measurements of RNA Structure Conservation with Generalized Centroid Estimators Reviewed International journal

    Yohei Okada, Yutaka Saito, Kengo Sato, Yasubumi Sakakibara

    Frontiers in Genetics   2   2011

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    Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Frontiers Media SA  

    DOI： 10.3389/fgene.2011.00054

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  • Prediction of RNA secondary structure by maximizing pseudo-expected accuracy Reviewed International journal

    Michiaki Hamada, Kengo Sato, Kiyoshi Asai

    BMC Bioinformatics   11 ( 1 )   2010.11

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

    DOI： 10.1186/1471-2105-11-586

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    Other Link： http://link.springer.com/article/10.1186/1471-2105-11-586/fulltext.html

  • Robust and accurate prediction of noncoding RNAs from aligned sequences Reviewed International journal

    Yutaka Saito, Kengo Sato, Yasubumi Sakakibara

    BMC Bioinformatics   11 ( S7 )   2010.10

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

    DOI： 10.1186/1471-2105-11-s7-s3

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    Other Link： http://link.springer.com/article/10.1186/1471-2105-11-S7-S3/fulltext.html

  • Improving the accuracy of predicting secondary structure for aligned RNA sequences Reviewed International journal

    Michiaki Hamada, Kengo Sato, Kiyoshi Asai

    Nucleic Acids Research   39 ( 2 )   393 - 402   2010.9

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

    DOI： 10.1093/nar/gkq792

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  • RactIP: fast and accurate prediction of RNA-RNA interaction using integer programming Reviewed International journal

    Yuki Kato, Kengo Sato, Michiaki Hamada, Yoshihide Watanabe, Kiyoshi Asai, Tatsuya Akutsu

    Bioinformatics   26 ( 18 )   i460 - i466   2010.9

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

    DOI： 10.1093/bioinformatics/btq372

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  • A NON-PARAMETRIC BAYESIAN APPROACH FOR PREDICTING RNA SECONDARY STRUCTURES Reviewed International journal

    KENGO SATO, MICHIAKI HAMADA, TOUTAI MITUYAMA, KIYOSHI ASAI, YASUBUMI SAKAKIBARA

    Journal of Bioinformatics and Computational Biology   08 ( 04 )   727 - 742   2010.8

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：World Scientific Pub Co Pte Ltd  

    DOI： 10.1142/s0219720010004926

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  • Improved prediction of transcription binding sites from chromatin modification data Reviewed International coauthorship International journal

    Kengo Sato, Tom Whitington, Timothy L. Bailey, Paul Horton

    2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology   1 - 7   2010.5

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    Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/cibcb.2010.5510323

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  • CentroidAlign: fast and accurate aligner for structured RNAs by maximizing expected sum-of-pairs score Reviewed International journal

    Michiaki Hamada, Kengo Sato, Hisanori Kiryu, Toutai Mituyama, Kiyoshi Asai

    Bioinformatics   25 ( 24 )   3236 - 3243   2009.10

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

    DOI： 10.1093/bioinformatics/btp580

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  • GRADIENT-BASED OPTIMIZATION OF HYPERPARAMETERS FOR BASE-PAIRING PROFILE LOCAL ALIGNMENT KERNELS Reviewed International journal

    KENGO SATO, YUTAKA SAITO, YASUBUMI SAKAKIBARA

    Genome Informatics 2009   128 - 138   2009.10

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO.  

    DOI： 10.1142/9781848165632_0012

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  • IMPROVEMENT OF STRUCTURE CONSERVATION INDEX WITH CENTROID ESTIMATORS Reviewed International journal

    YOHEI OKADA, KENGO SATO, YASUBUMI SAKAKIBARA

    Biocomputing 2010   88 - 97   2009.10

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    Authorship：Corresponding author   Language：English   Publishing type：Part of collection (book)   Publisher：WORLD SCIENTIFIC  

    DOI： 10.1142/9789814295291_0011

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  • Predictions of RNA secondary structure by combining homologous sequence information Reviewed International journal

    Michiaki Hamada, Kengo Sato, Hisanori Kiryu, Toutai Mituyama, Kiyoshi Asai

    Bioinformatics   25 ( 12 )   i330 - i338   2009.5

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

    DOI： 10.1093/bioinformatics/btp228

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  • CENTROIDFOLD: a web server for RNA secondary structure prediction Reviewed International coauthorship International journal

    K. Sato, M. Hamada, K. Asai, T. Mituyama

    Nucleic Acids Research   37 ( Web Server )   W277 - W280   2009.5

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    Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

    DOI： 10.1093/nar/gkp367

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  • Genome-wide searching with base-pairing kernel functions for noncoding RNAs: computational and expression analysis of snoRNA families in Caenorhabditis elegans Reviewed International journal

    K. Morita, Y. Saito, K. Sato, K. Oka, K. Hotta, Y. Sakakibara

    Nucleic Acids Research   37 ( 3 )   999 - 1009   2009.1

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    DOI： 10.1093/nar/gkn1054

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  • Prediction of RNA secondary structure using generalized centroid estimators Reviewed International journal

    Michiaki Hamada, Hisanori Kiryu, Kengo Sato, Toutai Mituyama, Kiyoshi Asai

    Bioinformatics   25 ( 4 )   465 - 473   2008.12

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    DOI： 10.1093/bioinformatics/btn601

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  • Directed acyclic graph kernels for structural RNA analysis Reviewed International journal

    Kengo Sato, Toutai Mituyama, Kiyoshi Asai, Yasubumi Sakakibara

    BMC Bioinformatics   9 ( 1 )   2008.7

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

    DOI： 10.1186/1471-2105-9-318

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    Other Link： http://link.springer.com/article/10.1186/1471-2105-9-318/fulltext.html

  • Software.ncrna.org: web servers for analyses of RNA sequences Reviewed International journal

    K. Asai, H. Kiryu, M. Hamada, Y. Tabei, K. Sato, H. Matsui, Y. Sakakibara, G. Terai, T. Mituyama

    Nucleic Acids Research   36 ( Web Server )   W75 - W78   2008.5

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

    DOI： 10.1093/nar/gkn222

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  • STEM KERNELS FOR RNA SEQUENCE ANALYSES Reviewed International journal

    YASUBUMI SAKAKIBARA, KRIS POPENDORF, NANA OGAWA, KIYOSHI ASAI, KENGO SATO

    Journal of Bioinformatics and Computational Biology   05 ( 05 )   1103 - 1122   2007.10

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    Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：World Scientific Pub Co Pte Ltd  

    Several computational methods based on stochastic context-free grammars have been developed for modeling and analyzing functional RNA sequences. These grammatical methods have succeeded in modeling typical secondary structures of RNA, and are used for structural alignment of RNA sequences. However, such stochastic models cannot sufficiently discriminate member sequences of an RNA family from nonmembers and hence detect noncoding RNA regions from genome sequences. A novel kernel function, stem kernel, for the discrimination and detection of functional RNA sequences using support vector machines (SVMs) is proposed. The stem kernel is a natural extension of the string kernel, specifically the all-subsequences kernel, and is tailored to measure the similarity of two RNA sequences from the viewpoint of secondary structures. The stem kernel examines all possible common base pairs and stem structures of arbitrary lengths, including pseudoknots between two RNA sequences, and calculates the inner product of common stem structure counts. An efficient algorithm is developed to calculate the stem kernels based on dynamic programming. The stem kernels are then applied to discriminate members of an RNA family from nonmembers using SVMs. The study indicates that the discrimination ability of the stem kernel is strong compared with conventional methods. Furthermore, the potential application of the stem kernel is demonstrated by the detection of remotely homologous RNA families in terms of secondary structures. This is because the string kernel is proven to work for the remote homology detection of protein sequences. These experimental results have convinced us to apply the stem kernel in order to find novel RNA families from genome sequences.

    DOI： 10.1142/s0219720007003028

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  • PSSMTS: position specific scoring matrices on tree structures Reviewed International journal

    Kengo Sato, Kensuke Morita, Yasubumi Sakakibara

    Journal of Mathematical Biology   56 ( 1-2 )   201 - 214   2007.7

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

    DOI： 10.1007/s00285-007-0108-4

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    Other Link： http://link.springer.com/article/10.1007/s00285-007-0108-4/fulltext.html

  • RNA secondary structural alignment with conditional random fields Reviewed International journal

    Kengo Sato, Yasubumi Sakakibara

    Bioinformatics   21 ( suppl_2 )   ii237 - ii242   2005.9

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

    DOI： 10.1093/bioinformatics/bti1139

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  • Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures Reviewed International journal

    H. Matsui, K. Sato, Y. Sakakibara

    Bioinformatics   21 ( 11 )   2611 - 2617   2005.3

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    DOI： 10.1093/bioinformatics/bti385

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  • A new e-learning paradigm through annotating operations Reviewed International journal

    Hiroaki Saito, Hiroyuki Okamoto, Kyoko Ohara, Kengo Sato, Kazunari Ito, Shinsuke Hizuka, Masaya Soga, Tomoya Nishino, Yuji Nomura, Hideaki Shirakawa

    Proceedings of the Workshop on eLearning for Computational Linguistics and Computational Linguistics for eLearning - eLearn '04   86 - 89   2004

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    DOI： 10.3115/1610028.1610040

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  • Rongorongo character listing tool, Reviewed International journal

    Yamaguchi F., Nobesawa S., Sato K.

    Proc. of the 3rd IASTED International Conference on Visualization, Imaging and Image Processing   1   158 - 162   2003.9

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  • Preferential presentation of Japanese near-synonyms using definition statements Reviewed International journal

    Hiroyuki Okamoto, Kengo Sato, Hiroaki Saito

    Proceedings of the second international workshop on Paraphrasing -   16   17 - 24   2003

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    DOI： 10.3115/1118984.1118987

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  • Extracting Word Sequence Correspondences Based on Support Vector Machines. Reviewed

    KENGO SATO, HIROAKI SAITO

    Journal of Natural Language Processing   10 ( 4 )   109 - 124   2003

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    Authorship：Lead author,　Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Association for Natural Language Processing  

    DOI： 10.5715/jnlp.10.4_109

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  • The Use of Domain-Specific Statistical Data for Japanese Morphological Analysis, Reviewed

    Shiho Nobesawa, Kengo Sato, Hiroaki Saito

    Journal of Natural Language Processing   9 ( 3 )   21 - 40   2002.7

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    Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Association for Natural Language Processing  

    We propose two methods for the recognition of unknown strings in dictionary-based natural language processing systems. One method is for the dynamic use of statistical information during processing, and the other is for obtaining meaningful strings which should be added to the dictionary. Both methods are based on statistical information drawn from a training corpus, and there is no need for part-of-speech tagging or other preprocessing of the training corpus. We applied our methods to a Japanese morphological analysis system and had good results in reduction of unknown words and over segmentation.

    DOI： 10.5715/jnlp.9.3_21

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  • Authorship identification based on support vector machines, Reviewed International journal

    Yoshida A., Sato K., Nobesawa S., Saito H.

    Proc. of the 6th World Multiconference on Semantics, Cybernetics and Informatics (SCI 2002)   3   423 - 428   2002.7

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  • Extraction of Japanese web-site top-pages for the directory of the web, Reviewed International journal

    Kikuchi H., Sato K., Saito H.

    Proc. of the 6th World Multiconference on Semantics, Cybernetics and Informatics (SCI 2002)   3   400 - 405   2002.7

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  • Recognition of domain-specific terms with d-bigram model, Reviewed International journal

    Nobesawa S., Sato K., Saito H.

    Proc. of the 6th World Multiconference on Semantics, Cybernetics and Informatics (SCI 2002)   3   406 - 411   2002.7

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  • Extracting Bilingual Word Pairs with Maximum Entropy Modeling, Reviewed

    Kengo Sato, Hiroaki Saito

    Proc. of the 6th World Multiconference on Semantics, Cybernetics and Informatics (SCI 2002)   9 ( 1 )   101 - 115   2002.1

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    Authorship：Lead author,　Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Association for Natural Language Processing  

    Translation dictionaries used in multilingual natural language processing such as machine translation have been made manually, but a great deal of labor is required for this work and it is difficult to keep the description of the dictionaries consistent. Therefore, researches of extracting bilingual word pairs from parallel corpora automatically become active recently. In this paper, we propose a learning and extracting method of bilingual word pairs from aligned parallel corpora with the maximum entropy modeling. We define a probabilistic model of bilingual word pairs and four types of feature functions which express statistical and linguistic properties such as co-occurrence information and morphlogical information. Co-occurrence information restricts the sense of words. Morphlogical information restricts the part-of-speech of words. Experiment results in which Japanese and English parallel corpora are used show that our method performs better than the previous methods and can extract the bilingual word pairs which do not appear in the training corpus with almost the same accuracy as the appeared pairs due to the property of the maximum entropy modeling.

    DOI： 10.5715/jnlp.9.101

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  • Extracting word sequence correspondences with support vector machines Reviewed International journal

    Kengo Sato, Hiroaki Saito

    Proceedings of the 19th international conference on Computational linguistics -   1   1 - 7   2002

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computational Linguistics  

    DOI： 10.3115/1072228.1072248

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  • A visual system for education of machine language, Reviewed International journal

    Takaoka E., Maeda A., Sato K., Yamaguchi F., Nakanishi M.

    Proc. of the International Conference on Information and Communication Technologies for Education   289 - 298   2000.12

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  • Maximum entropy model learning of the translation rules, Reviewed International journal

    Sato K., Nakanishi M.

    Proc. of COLING/ACL '98   1171 - 1175   1998.8

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  • Segmenting sentences into linky strings using d-bigram statistics, Reviewed International journal

    Nobesawa S., Tsutsumi J., Sun D. J., Sano T., Sato K., Nakanishi M.

    Proc. of the 16th International Conference on Computational Linguistics (COLING '96)   586 - 591   1996.8

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Books

  • RNA Secondary Structure Prediction Based on Energy Models,

    Manato Akiyama, Kengo Sato（ Role： Joint author）

    Springer Science+Business Media  2023.1  （ ISBN:9781071627679 ）

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    Responsible for pages：89-105   Language：Japanese   Book type：Scholarly book

    DOI： 10.1007/978-1-0716-2768-6_6

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  • バイオインフォマティクス入門

    HSato Kengo, HR（ Role： Contributor第２章）

    慶應義塾大学出版会  2015.8  （ ISBN:9784766422511 ）

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    Language：Japanese   Book type：General book, introductory book for general audience

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  • Readings in Japanese Natural Language Processing Reviewed

    Nobesawa H.S., Sano T., Sato K., Saito, H.（ Role： ContributorDomain-specific statistical data for morphological analysis）

    Stanford Univ Center for the Study  2016.7 

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  • 実験医学増刊「生命科学の最先端に役立つバイオデータベースとウェブツール総集編」

    佐藤健吾, 榊原康文（ Role： Contributor二次構造に基づく機能性RNAの配列解析）

    羊土社  2008.8 

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    Responsible for pages：121-126   Language：Japanese   Book type：General book, introductory book for general audience

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  • Grammatical Inference: Algorithms and Applications,

    Yasubumi Sakakibara, Satoshi Kobayashi, Kengo Sato, Tetsuro Nishino, Etsuji Tomita（ Role： Joint editor）

    Springer  2006.9 

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  • Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

    Sakakibara, Y., Kobayashi, S., Sato, K., Nishino, T., Tomita, E.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  2006 

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MISC

  • AIを活用したmRNA配列の最適化 Invited

    佐藤健吾

    生体の科学   76 ( 2 )   2025.4

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    Authorship：Lead author,　Last author,　Corresponding author   Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (other)  

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  • MXfold2: 深層学習にもとづくRNAの高精度な二次構造予測プログラム, Invited

    佐藤健吾

    バイオサイエンスとインダストリー   79 ( 6 )   2021.11

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    Authorship：Lead author,　Last author,　Corresponding author   Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (other)  

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  • 世界最高精度のRNA二次構造予測を達成 --- 熱力学モデルと深層学習の効果的な組合せ, Invited

    佐藤健吾

    化学   76 ( 7 )   2021.7

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  • Introduction to Selected Papers from GIW2018

    Li J, Nakai K, Zheng Y, Sato K, Wong L

    Journal of Bioinformatics and Computational Biology   16 ( 6 )   2018.12

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    Publisher：Journal of Bioinformatics and Computational Biology  

    DOI： 10.1142/S0219720018020055

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  • がん細胞株におけるderived RNAのプロファイル解析

    青木言太, 土谷麻里子, 小坂威雄, 長谷純崇, 佐藤健吾, 水野隆一, 大家基嗣, 榊原康文

    日本RNA学会年会要旨集   19th   2017

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  • RF-002 Construction of a question-answering program that automatically answers the medical licensing examination

    Ito Shino, Tanaka Yugaku, Sato Kengo, Ko Shigeru, Kano Yoshinobu, Sakakibara Yasubumi

    14 ( 2 )   11 - 16   2015.8

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    Language：Japanese   Publisher：Forum on Information Technology  

    CiNii Books

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  • Prediction of gene structures from RNA-seq data using dual decomposition (情報論的学習理論と機械学習)

    INATSUKI TATSUMU, SATO KENGO, SAKAKIBARA YASUBUMI

    電子情報通信学会技術研究報告 = IEICE technical report : 信学技報   115 ( 112 )   323 - 326   2015.6

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  • Prediction of gene structures from RNA-seq data using dual decomposition (ニューロコンピューティング)

    INATSUKI TATSUMU, SATO KENGO, SAKAKIBARA YASUBUMI

    電子情報通信学会技術研究報告 = IEICE technical report : 信学技報   115 ( 111 )   207 - 210   2015.6

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  • Genome sequence of the luminous mushroom Mycena chlorophos for searching fungal bioluminescence genes

    Yugaku Tanaka, Daisuke Kasuga, Yumiko Oba, Sumitaka Hase, Kengo Sato, Yuichi Oba, Yasubumi Sakakibara

    LUMINESCENCE   29   47 - 48   2014.8

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    Language：English   Publishing type：Research paper, summary (international conference)  

    Web of Science

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  • 機械学習を用いたマススペクトルデータからの糖鎖構造推定法の開発

    雲崎翔太郎, 佐藤健吾, 榊原康文

    研究報告バイオ情報学（BIO）   2013 ( 30 )   1 - 2   2013.6

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    近年，タンパク質を介しさまざまな調節を受けて生合成される糖鎖を扱うグライコミクス研究が世界中で盛んに行われ急速な技術発展を遂げている．現在では多糖構造の同定においてタンデムマススペクトロメトリー （MS/MS） が主要なツールとなっている．しかしマススペクトルデータからその糖鎖構造を解析して決定する計算手法の開発は少なく，汎用的なソフトウェアは少ない．先行研究において，構造を決定する上で有用な開裂イオンが持つ情報を用い，動的計画法を利用することで，マススペクトルデータから糖鎖構造を決定する手法が提案された．しかし，スコア関数が最適化されていない，複雑な構造が扱えない，スペクトルデータ中のノイズに弱いなどの問題点があった．そこで本研究では，マススペクトルを生成する機械のノイズや偏りを統計的機械学習手法によって学習することによって，マススペクトルデータから糖鎖構造をより正確に解析できるアルゴリズムを開発した．

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  • Temporal transcriptome analysis for carcinogenesis process by digital clustering.

    青戸良賢, 八谷剛史, 奥村和弘, 長谷純崇, 佐藤健吾, 若林雄一, 榊原康文

    研究報告バイオ情報学（BIO）   2013 ( 8 )   1 - 6   2013.3

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    Language：Japanese  

    発がんマウス実験系を用いて採取した正常上皮，良性腫瘍，悪性腫瘍，転移性腫瘍の 4 ステージに対して Illumina Genome Analyzer IIx による mRNA-Seq を行い，Student&#039;s t-test を用いて各ステージにおける発現差異遺伝子を探索した．本研究では，探索された発現差異遺伝子群の経時的トランスクリプトーム動態を解明するため，デジタルクラスタリングという手法を開発した．4 ステージ間計 6 （= 4C2） 通りの検定結果をデジタル化した 6 次元ベクトルを構築し，これをマンハッタン距離を用いたウォード法による階層的クラスタリングを行うことで，同じ検定結果を有する遺伝子群を同一のクラスタに分類することができ，またクラスタ間で共通の検定結果を優先した階層的クラスタリングを行うことができる．本研究で得られた発現差異遺伝子候補群に対し既存のクラスタリング手法と比較を行った結果，既存手法では得ることができない，検定結果に基づいたクラスタが得られた．We developed the so called &quot;digital clustering&quot; method for analysis of temporal transcriptome dynamics of differential expression genes in carcinogenesis process. We obtained normal skin, papilloma, carcinoma, and metastasis by experimental carcinogenesis to mice, and sequenced mRNA by Illumina Genome Analyzer IIx. For searching differentially expressed genes, the statistical test, Student&#039;s t-test, was applied to the expression levels for each genes. We digitalized the results of total 6 tests, all pairwise combinations from 4 stages constitute six dimensional vectors. Digital clustering adopts hierarchical clustering with ward&#039;s method, using Manhattan distance, and by using 6 dimentional vectors, it can not only classify genes that have same test results into same cluster but also give priority to the common test results between clusters. The experiments on differential expression genes detected our method showed that digital clustering can provide significant clusters based on the statistical test results not provided by other existing methods.

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  • A Modified Genovo Metagenome Assembler for 454 Paired End Reads

    2012 ( 2 )   1 - 6   2012.10

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  • RNA structural alignments via dual decomposition

    Kengo Sato, Yuki Kato, Tatsuya Akutsu, Kiyoshi Asai, Yasubumi Sakakibara

    IPSJ SIG technical reports   2012 ( 5 )   1 - 6   2012.3

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    Language：Japanese   Publisher：Information Processing Society of Japan (IPSJ)  

    We develop DAFS, a novel algorithm that simultaneously aligns and folds RNA sequences based on maximizing expected accuracy of a predicted common secondary structure and its alignment. DAFS decomposes the pairwise structural alignment problem into two independent secondary structure prediction problems and one pairwise (non-structural) alignment problem by the dual decomposition technique, and maintains the consistency of a pairwise structural alignment by imposing penalties on inconsistent base pairs and alignment columns that are iteratively updated. The experiments on publicly available datasets showed that DAFS can produce reliable structural alignments from unaligned sequences in terms of accuracy of common secondary structure prediction.

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  • RNA Pseudoknot Prediction Based on Maximizing Expected Accuracy

    SATO KENGO, KATO YUKI, AKUTSU TATSUYA, ASAI KIYOSHI

    2010 ( 6 )   1 - 6   2010.7

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  • RNA-RNA interaction prediction using integer programming with threshold cut (ニューロコンピューティング)

    Kato Yuki, Sato Kengo, Hamada Michiaki

    電子情報通信学会技術研究報告   110 ( 83 )   183 - 190   2010.6

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  • RNA-RNA Interaction Prediction Using Integer Programming with Threshold Cut

    KATO YUKI, SATO KENGO, HAMADA MICHIAKI, WATANABE YOSHIHIDE, ASAI KIYOSHI, AKUTSU TATSUYA

    2010 ( 32 )   1 - 8   2010.6

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  • Sequence and Structural Analyses for Functional non-coding RNAs, Reviewed International journal

    Sakakibara Y., Sato K.

    Algorithmic Bioprocesses   2009.8

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    Authorship：Last author   Language：English   Publishing type：Article, review, commentary, editorial, etc. (other)   Publisher：Springer  

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  • CentroidFold:RNA二次構造予測ウェブサーバー

    佐藤健吾, 佐藤健吾, 浜田道昭, 浜田道昭, 浅井潔, 浅井潔, 光山統泰

    日本RNA学会年会要旨集   11th   96   2009.7

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    J-GLOBAL

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  • Base-pairing profile local alignment kernels for functional RNA analyses (ニューロコンピューティング)

    Sato Kengo, Saito Yutaka, Sakakibara Yasubumi

    電子情報通信学会技術研究報告   109 ( 53 )   45 - 51   2009.5

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  • Base-pairing profile local alignment kernels for functional RNA analyses Reviewed

    Kengo Sato, Yutaka Saito, Yasubumi Sakakibara

    情報処理学会研究報告バイオ情報学（BIO）   2009-BIO-17 ( 8 )   1 - 7   2009

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  • CentroidHomfold:相同配列群の情報を利用したRNAの2次構造予測

    浜田道昭, 浜田道昭, 佐藤健吾, 佐藤健吾, 木立尚孝, 木立尚孝, 光山統泰, 浅井潔, 浅井潔

    日本分子生物学会年会講演要旨集   32nd ( Vol.1 )   48   2009

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    J-GLOBAL

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  • 期待精度を最大化するRNA情報解析手法の開発

    浜田道昭, 浜田道昭, 木立尚孝, 佐藤健吾, 佐藤健吾, 光山統泰, 浅井潔, 浅井潔

    生化学   2P-0776   2008

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    J-GLOBAL

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  • 機能性RNAの配列解析と構造解析 Invited

    榊原康文, 佐藤健吾

    人工知能学会誌   22 ( 1 )   54 - 62   2007.1

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    Other Link： http://id.nii.ac.jp/1004/00006633/

  • d-bigram と他の統計情報との関連に関する実験

    堤 純也, 孫 大江, 延澤 志保, 佐藤 健吾, 佐野 智久, 中西 正和

    言語処理学会年次大会発表論文集 = Proceedings of the ... annual meeting of the Association for Natural Language Processing   2   189 - 192   1996.3

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  • A Sentence-Unit Pinyin-Hanzi Conversion Using Statistical Information

    SUN Da Jiang, TSUTSUMI Junya, NOBESAWA Shiho, SANO Tomohisa, SATO Kengo, OOMORI Kumiko, NAKANISHI Masakazu

    2   53 - 56   1996.3

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  • An Experiment on Good Usages of D-bigram Statistics in Natural Language Evaluation

    SANO Tomohisa, TSUTSUMI Junya, SUN Da Jiang, NOBESAWA Shiho, SATO Kengo, OOMORI Kumiko, NAKANISHI Masakazu

    2   185 - 188   1996.3

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    CiNii Books

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  • Word Clustering Using D-bigram

    51   7 - 8   1995.9

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    Language：Japanese  

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▼display all

Presentations

  • Recent advances in RNA secondary structure prediction with machine learning and deep learning Invited International conference

    Kengo Sato

    Computational Approaches to RNA Structure and Function  2024.7 

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    Event date： 2024.7 - 2024.8

    Language：English   Presentation type：Oral presentation (invited, special)  

    Venue：Benasque   Country：Spain  

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  • 深層学習の生命科学応用とRNA二次構造予測の最新研究 Invited

    佐藤健吾

    第81回産総研AIセミナー  2024.12 

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    Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

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  • CASP16参加報告：RNAおよびRNA-タンパク質複合体の立体構造予測の現状と課題 Invited

    佐藤健吾

    AIが変える創薬とタンパク質構造予測の最前線シンポジウム  2024.12 

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    Language：Japanese   Presentation type：Symposium, workshop panel (nominated)  

    Venue：広島工業大学   Country：Japan  

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  • 機械学習および深層学習による RNA二次構造予測の最新動向 Invited

    佐藤健吾

    mRNAターゲット創薬機構講演会  2025.6 

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    Language：Japanese   Presentation type：Oral presentation (invited, special)  

    Venue：千葉   Country：Japan  

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  • フラグメントの重ね合わせと最適化に基づく粗視化RNA構造からの全原子構造の生成 International coauthorship

    築山翔, 倉田博之, 佐藤健吾, Yang Zhang

    情報処理学会第82回バイオ情報学研究会  2025.6 

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    Event date： 2025.6

    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：琉球大学   Country：Japan  

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  • Cell type-specific functions of nucleic acid-binding proteins revealed by deep learning on co-expression networks

    大里直樹, 佐藤健吾

    情報処理学会第82回バイオ情報学研究会  2025.6 

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    Event date： 2025.6

    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：琉球大学   Country：Japan  

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  • 遺伝的アルゴリズムを用いたmRNA配列設計

    高谷勇輝, 佐藤健吾

    情報処理学会第81回バイオ情報学研究会  2025.3 

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    Event date： 2025.3

    Language：Japanese   Presentation type：Oral presentation (general)  

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  • Participation Report on RNA 3D structure prediction at CASP16: Usage and limitations of AlphaFold3 International conference

    Junichi Iwakiri, Takumi Otagaki, Kazuteru Yamamura, Shunsuke Sumi, Ikuo Kurisaki, Jiro Kondo, Kengo Sato

    1st Asia & Pacific Bioinformatics Joint Conference  2024.10 

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    Event date： 2024.10

    Language：English   Presentation type：Poster presentation  

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  • Ongoing Participation Report on RNA 3D structure prediction at CASP16: Usage and limitations of AlphaFold3

    Junichi Iwakiri, Shunsuke Sumi, Takumi Otagaki, Kazuteru Yamamura, Jiro Kondo, Kengo Sato

    2024.6 

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  • 拡散モデルによるRNAアプタマー配列の設計

    松本英倫, 佐藤健吾

    情報処理学会第76回バイオ情報学研究会  2024.3 

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    Event date： 2024.3

    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：北陸先端科学技術大学院大学   Country：Japan  

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  • 深層学習によるRNA二次構造予測MXfold2の機能追加と性能改善

    佐藤健吾

    RNAインフォマティクス道場2023  2023.8 

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    Event date： 2023.8

    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：兵庫県神戸市  

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  • 深層学習に基づく RNA グアニン4重鎖構造識別法の検討 International coauthorship

    加藤有己, 佐藤健吾, Jakob Hull Havgaard, 河原行郎

    第20回日本RNA学会年会  2018.7 

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    Event date： 2018.7

    Language：Japanese   Presentation type：Poster presentation  

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  • RNA secondary structure prediction using deep learning

    2017.9 

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    Event date： 2017.9

    Language：English   Presentation type：Poster presentation  

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  • がん細胞株における derived RNA のプロファイル解析

    青木言太, 土谷麻里子, 小坂威雄, 長谷純崇, 佐藤健吾, 水野隆一, 大家基嗣, 榊原康文

    第19回日本RNA学会年会  2017.7 

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    Event date： 2017.7

    Language：Japanese   Presentation type：Oral presentation (general)  

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  • 深層学習によるRNA二次構造予測

    秋山真那斗, 榊原康文, 佐藤健吾

    第19回日本RNA学会年会  2017.7 

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    Event date： 2017.7

    Language：Japanese   Presentation type：Poster presentation  

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  • 医師国家試験自動解答プログラムの治療薬問題への拡張

    水口達矢, 伊藤詩乃, 佐藤健吾, 榊原康文

    第31回人工知能学会全国大会  2017.5 

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    Event date： 2017.5

    Language：Japanese   Presentation type：Oral presentation (general)  

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  • Improving RNA secondary structure prediction with weak label learning from NGS data

    Akiyama, M., Sakakibara, Y., Sato, K.

    第5回生命医薬情報学連合大会  2016.9  日本バイオインフォマティクス学会

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    Event date： 2016.9

    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：プラザ平成（東京都江東区）  

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  • Inverse folding of two interacting RNA molecules

    Taneda A., Sato, K.

    第5回生命医薬情報学連合大会  2016.9  日本バイオインフォマティクス学会

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    Event date： 2016.9

    Language：English   Presentation type：Poster presentation  

    Venue：プラザ平成（東京都江東区）  

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  • Accurate prediction of RNA-RNA interactions from sequence data incorporating interaction site accessibility International conference

    Kato, Y., Mori, T., Sato, K., Maegawa, S., Hosokawa, H., Akutsu, T.

    The 21st Annual meeting of the RNA Society (RNA2016)  2016.6 

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    Event date： 2016.6

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  • Rtools: a web server for various secondary structural analyses on single RNA sequences International conference

    Hamada, M., Ono, Y., Kiryu, H., Sato, K., Kato, Y., Fukunaga, T., Mori, R., Asai, K.

    The 21st Annual meeting of the RNA Society (RNA2016)  2016.6 

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    Event date： 2016.6

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  • A max-margin model for predicting residue-base contacts in protein-RNA interactions

    Sato, K., Kashiwagi, S., Sakakibara, Y.

    第 4 回生命医薬情報学連合大会、日本バイオインフォマティクス学会 2015 年年会  2015.10  第4回生命医薬情報学連合大会、日本バイオインフォマティクス学会 2015 年年会

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    Event date： 2015.10

    Language：English   Presentation type：Poster presentation  

    Venue：京都大学宇治キャンパス、京都府宇治市   Country：Japan  

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  • 双対分解によるマルチプルアラインメント

    穴水拓郎, 榊原康文, 佐藤健吾

    第 4 回生命医薬情報学連合大会,日本バイオインフォマティクス学会 2015 年年会  2015.10 

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    Event date： 2015.10

    Language：Japanese   Presentation type：Poster presentation  

    Venue：京都大学宇治キャンパス、京都府宇治市   Country：Japan  

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  • 医師国家試験を自動解答するプログラムの構築

    伊藤詩乃, 田中佑岳, 佐藤健吾, 洪繁, 狩野芳伸, 榊原康文

    第 14 回情報科学技術フォーラム  2015.9 

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    Event date： 2015.9

    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：愛媛県松山市   Country：Japan  

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  • 統計的検定結果に基づく高精度クラスタリングアルゴリズムの開発

    青戸良賢, 八谷剛史, 奥村和弘, 長谷純崇, 佐藤健吾, 若林雄一, 榊原康文

    情報処理学会第43回バイオ情報学研究会  2015.9 

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    Event date： 2015.9

    Language：Japanese   Presentation type：Oral presentation (general)  

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  • 双対分解によるマルチプルアラインメント

    穴水拓郎, 榊原康文, 佐藤健吾

    情報処理学会第43回バイオ情報学研究会  2015.9 

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    Event date： 2015.9

    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：慶應義塾大学矢上キャンパス、神奈川県横浜市   Country：Japan  

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  • A max-margin model for predicting residue-base contacts in protein-RNA interactions International conference

    Sato, K.

    the International Workshop on Computational Analysis of RNA Structure and Function  2015.7 

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    Event date： 2015.7

    Language：English   Presentation type：Oral presentation (general)  

    Venue：Benasque   Country：Spain  

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  • Intensive deep learning on GPUs for the prediction of anticancer drug sensitivity International conference

    Abe, M., Haruyama, K., Sugawara, Y., Sato, K., Sakakibara, Y.

    HitSeq 2015, a SIG of ISMB/ECCB 2015  2015.7 

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    Event date： 2015.7

    Language：English   Presentation type：Poster presentation  

    Venue：Dublin   Country：Ireland  

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  • 機械学習を用いたタンパク質とRNAのコンタクト予測

    佐藤健吾, 柏木駿也, 榊原康文

    第3回生命医薬情報学連合大会，日本バイオインフォマティクス学会2014年年会  2014.10 

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    Event date： 2014.10

    Language：Japanese   Presentation type：Poster presentation  

    Venue：仙台   Country：Japan  

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  • Genome sequence of the luminous mushroom Mycena chlorophos for searching fungal bioluminescence genes International conference

    Tanaka, Yugaku, Kasuga Daisuke, Oba Yumiko, Hase Sumitaka, Sato Kengo, Oba, Yuichi, Sakakibara, Yasubumi

    LUMINESCENCE  2014.8 

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    Event date： 2014.8

    Language：English   Presentation type：Oral presentation (general)  

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  • 機械学習を用いたタンパク質とRNAのコンタクト予測

    佐藤健吾, 柏木駿也, 榊原康文

    第16回日本RNA学会年会  2014.7 

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    Event date： 2014.7

    Language：Japanese   Presentation type：Poster presentation  

    Venue：名古屋   Country：Japan  

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  • DAFS: simultaneous aligning and folding of RNA sequences via dual decomposition International conference

    Sato, K., Kato, Y., Akutsu, T., Asai, K., Sakakibara, Y.

    International Symposium on Genome Science, Expanding Frontiers of Genome Science  2013.1 

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    Event date： 2013.1

    Language：English   Presentation type：Poster presentation  

    Venue：Tokyo   Country：Japan  

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  • Simultaneous Aligning and Folding of RNA Sequences via Dual Decomposition Invited International conference

    Sato, K

    2012 Sapporo Workshop on Machine Learning and Applications to Biology  2012.8 

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    Event date： 2012.8

    Language：English   Presentation type：Oral presentation (invited, special)  

    Venue：Sappro   Country：Japan  

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  • DAFS: simultaneous aligning and folding of RNA sequences via dual decomposition International conference

    Sato, K

    International Workshop on RNA  2012.7 

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    Event date： 2012.7

    Language：English   Presentation type：Oral presentation (general)  

    Venue：Benasque   Country：Spain  

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  • Prediction of RNA secondary structures with generalized centroid estimators International conference

    Sato, K

    International Workshop on Computational Methods for RNA Analysis  2009.7 

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    Event date： 2009.7

    Language：English   Presentation type：Oral presentation (general)  

    Venue：Benasque   Country：Spain  

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  • RNA secondary structural alignment with conditional random fields International conference

    Sato, K

    International Workshop on Computational Approaches to Functional and Regulatory RNAs  2006.7 

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    Event date： 2006.7

    Language：English   Presentation type：Oral presentation (general)  

    Venue：Benasque   Country：Spain  

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  • Ongoing Participation Report on RNA 3D structure prediction at CASP16: Usage and limitations of AlphaFold3

    Junichi Iwakiri, Takumi Otagaki, Kazuteru Yamamura, Shunsuke Sumi, Ikuo Kurisaki, Jiro Kondo, Kengo Sato

    2024.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

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  • RNA secondary structure prediction using deep learning with thermodynamic integration International conference

    Sato, K., Akiyama, M., Sakakibara, Y.

    Noncoding RNAs: Biology and Applications  2021.5  Keystone Symposia

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    Language：English   Presentation type：Poster presentation  

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  • プライバシー保護技術を用いた遺伝子発現差異解析

    Kawaguchi, K., Sakakibara, Y., Sato, K.

    第10回生命医薬情報学連合大会，日本バイオインフォマティクス学会2021年年会  2021.9 

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    Language：Japanese   Presentation type：Poster presentation  

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  • Prediction of RNA secondary structure including pseudoknots for long sequences

    Kengo Sato, Yuki Kato

    第68回バイオ情報学研究会  2021.11  情報処理学会

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    Language：Japanese   Presentation type：Oral presentation (general)  

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  • MXfold2: 深層学習を用いたRNA二次構造予測 Invited

    佐藤健吾,秋山真那斗,榊原康文

    第44回日本分子生物学会年会  2021.12 

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    Language：Japanese   Presentation type：Symposium, workshop panel (nominated)  

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  • Extending a deep learning-based RNA secondary structure prediction algorithm for RNA modifications

    Naoki Mikamo, Yasubumi Sakakibara and Kengo Sato

    第23回日本RNA学会年会  2022.7  日本RNA学会

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    Language：Japanese   Presentation type：Poster presentation  

    Venue：京都  

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  • RNA secondary structure prediction using deep learning with thermodynamic integration International conference

    Sato, K., Akiyama, M., Sakakibara, Y.

    RNA meeting 2021  2021.6 

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  • RNA secondary structure prediction using deep learning with thermodynamic integration International conference

    Sato, K., Akiyama, M., Sakakibara, Y.

    The 29th Intelligent Systems for Molecular Biology and the 20th European Conference on Computational Biology (ISMB/ECCB 2021)  2021.7 

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  • Deep learning-based prediction of potential RNA G-quadruplexes with D-Quartet International coauthorship International conference

    Kato, Y., Sato, K., Havgaard, JH., Kawahara, Y.

    The 29th Intelligent Systems for Molecular Biology and the 20th European Conference on Computational Biology (ISMB/ECCB 2021)  2021.7 

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  • RNA secondary structure prediction using deep learning with thermodynamic integration,

    Sato, K., Akiyama, M., Sakakibara, Y.

    第10回生命医薬情報学連合大会，日本バイオインフォマティクス学会2021年年会  2021.9 

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  • Extending a deep learning-based RNA secondary structure prediction algorithm for RNA modifications

    Naoki Mikamo, Yasubumi Sakakibara and Kengo Sato

    第11回生命医薬情報学連合大会 (IIBMP 2022)  2022.9  日本バイオインフォマティクス学会

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    Language：Japanese   Presentation type：Poster presentation  

    Venue：豊中市  

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  • 熱力学モデルを統合した深層学習によるRNA二次構造予測 Invited

    佐藤健吾

    第21回情報科学技術フォーラム（FIT2022）  2022.9  電子情報通信学会、情報処理学会

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    Language：Japanese   Presentation type：Oral presentation (invited, special)  

    Venue：慶應義塾大学 矢上キャンパス   Country：Japan  

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  • Extending a deep learning-based RNA secondary structure prediction algorithm for RNA modifications

    Naoki MIkamo, Yasubumi Sakakibara, Kengo Sato

    情報処理学会第72回バイオ情報学研究会  2022.11 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：東京工業大学大岡山キャンパス  

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  • RNA secondary structure prediction using deep learning with thermodynamic integration International conference

    Kengo Sato, Manato Akiyama, Yasubumi Sakakibara

    RNA Nanotechnology (Gordon Research Conference)  2023.1 

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    Language：English   Presentation type：Poster presentation  

    Venue：Ventura, CA   Country：United States  

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  • プライバシー保護技術を用いた遺伝子発現差異解析

    川口開登, 榊原康文, 佐藤 健吾

    情報処理学会第73回バイオ情報学研究会  2023.3 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：北陸先端科学技術大学院大学   Country：Japan  

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  • 深層学習によるRNA二次構造予測法MXfold2の精度改善

    佐藤健吾

    情報処理学会第75回バイオ情報学研究会  2023.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：大阪公立大学中百舌鳥キャンパス   Country：Japan  

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Awards

  • 第9回生命医薬情報学連合大会 ポスター賞

    2020.9   日本バイオインフォマティクス学会  

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    Award type：Award from Japanese society, conference, symposium, etc.  Country：Japan

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  • 第6回生命医薬情報学連合大会 ポスター賞

    2017.10   日本バイオインフォマティクス学会  

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    Award type：Award from Japanese society, conference, symposium, etc.  Country：Japan

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  • 第5回生命医薬情報学連合大会 研究奨励賞

    2016.10   日本バイオインフォマティクス学会  

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    Award type：Award from Japanese society, conference, symposium, etc.  Country：Japan

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  • 山下記念研究賞

    2013.3   情報処理学会  

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    Award type：Award from Japanese society, conference, symposium, etc.  Country：Japan

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  • Oxford Jornals JSBi Prize

    2008.12   日本バイオインフォマティクス学会  

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    Award type：Award from Japanese society, conference, symposium, etc.  Country：Japan

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Research Projects

  • ハイスループット構造データを活用した深層学習による階層型RNA立体構造予測

    Grant number：25H01166  2025.4 - 2029.3

    日本学術振興会  科学研究費助成事業  基盤研究(A)

    佐藤 健吾, 近藤 次郎, 岩切 淳一

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    Grant amount：\46020000 （ Direct Cost: \35400000 、 Indirect Cost：\10620000 ）

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  • 多様な微生物機能の開拓のためのバイオものづくりDBTL技術の開発

    2024.4 - 2029.3

    科学技術振興機構  革新的GX技術創出事業

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    Authorship：Coinvestigator(s)  Grant type：Competitive

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  • 多層的生体情報の統合による疾患予防デジタルツインの構築

    2024.4 - 2029.3

    科学技術振興機構  未来社会創造事業

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    Authorship：Coinvestigator(s)  Grant type：Competitive

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  • 化学修飾を含むmRNA配列設計の基盤技術

    Grant number：24H00737  2024.4 - 2028.3

    日本学術振興会  科学研究費助成事業  基盤研究(A)

    浅井 潔, 佐藤 健吾, 上田 宏生, 阿部 洋

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    Authorship：Coinvestigator(s) 

    Grant amount：\47970000 （ Direct Cost: \36900000 、 Indirect Cost：\11070000 ）

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  • 生物情報アーカイブを活用した深層生成モデルによるmRNA最適設計技術

    2023.10 - 2029.3

    科学技術振興機構  戦略的創造研究推進事業CREST

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    Authorship：Coinvestigator(s)  Grant type：Competitive

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  • バイオものづくりのためのmRNA深層生成モデル

    2023.10 - 2024.3

    科学技術振興機構  革新的GX技術創出事業

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    Authorship：Principal investigator  Grant type：Competitive

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  • 修飾塩基を含むRNAの二次構造解析技術の確立

    Grant number：23K24944  2022.4 - 2025.3

    日本学術振興会  科学研究費助成事業  基盤研究(B)

    佐藤 健吾, 加藤 有己, 河原 行郎

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    Grant amount：\13780000 （ Direct Cost: \10600000 、 Indirect Cost：\3180000 ）

    本研究課題ではRNA修飾を考慮した二次構造予測を実現する高度なアルゴリズムを開発する．本研究がベースにするMXfold2は，深層ニューラルネットワークによる熱力学パラメータの精緻化によって高精度化を図る一方，深層ニューラルネットワークが計算するスコアと既存の熱力学パラメータを統合することによって過学習の影響を最小限に抑えて未知RNA配列に対する頑健性を向上させることに成功し，その結果RNA二次構造予測において世界最高精度を達成した．MXfold2は入力として4種類の正規な塩基 (A, C, G, U) にのみ対応しており，本研究ではこれをm6A，シュードウリジン，イノシンなどの修飾塩基に拡張する．具体的には，それぞれ4種類の塩基を表す4ビットのone-hot表現による入力から，修飾塩基の化学構造式を表すN (=1024) ビットのフィンガープリント表現による入力に変更する．フィンガープリント表現のそれぞれのビットは特定の部分構造の有無を表している．これによって塩基同士の化学的な類似度をモデルに埋め込むことが可能となり，訓練データで出現頻度が低い修飾塩基については，類似した塩基の特徴量からの類推を期待できる．これまでに本手法のプロトタイプ実装を行なった．さらに，完全な二次構造ではなく，ケミカルプロービングにより得られる二次構造プロファイルから二次構造予測モデルのパラメータを学習する手法を開発し，シミュレーションデータにおいてその検証を行なった結果，十分な精度を得られることを確認した．

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  • 修飾塩基を含むRNAの二次構造解析技術の確立

    Grant number：22H03689  2022.4 - 2025.3

    日本学術振興会  基盤研究(B)  基盤研究(B)

    佐藤 健吾, 加藤 有己, 河原 行郎

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    Authorship：Principal investigator 

    Grant amount：\13780000 （ Direct Cost: \10600000 、 Indirect Cost：\3180000 ）

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  • GenomeGAN: in silico genome design with generative adversarial networks

    Grant number：19K22897  2019.6 - 2022.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Challenging Research (Exploratory)

    Sato Kengo

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    Grant amount：\5200000 （ Direct Cost: \4000000 、 Indirect Cost：\1200000 ）

    In order to generate a genome sequence with specific traits, we tackled the RNA sequence design problem of designing an RNA sequence that forms specific secondary structures. By using deep reinforcement learning as a learning method for optimizing the search of sequence space, more efficient generation of sequences for the target secondary structure is achieved. The optimization method for converting discrete nucleotide sequences into a differentiable representation using Activation Maximization was applied to the RNA sequence design problem. We improved IPknot, a method for predicting RNA secondary structure including pseudoknot structures, to achieve linear computational time with respect to sequence length.

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  • RNA secondary structure prediction using nanopore sequencers

    Grant number：19H04210  2019.4 - 2022.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

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    Authorship：Principal investigator 

    Grant amount：\13260000 （ Direct Cost: \10200000 、 Indirect Cost：\3060000 ）

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  • Deep analysis of chemical communication space using artificial intelligence technology

    Grant number：17H06410  2017.6 - 2022.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)

    Sakakibara Yasubumi

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    Authorship：Coinvestigator(s) 

    Grant amount：\76180000 （ Direct Cost: \58600000 、 Indirect Cost：\17580000 ）

    The purpose of this research is to develop a model that represents a wide variety of chemical communication in a unified manner. We have developed the next-generation COPICAT, which is a virtual screening system that comprehensively and highly accurately predicts protein-compound interactions, and achieved higher accuracy than the state-of-the-art existing methods. We have developed a variational auto-encoder (NP-VAE) for handling natural compounds and succeeded in acquiring a chemical latent space that encodes natural macromolecular structures. A latent space of natural compounds and macromolecular structures was constructed using 1,900 types of compound data provided from the members of this research project. We succeeded in discovering a large number of new PKC ligand candidates through machine learning and expert domain knowledge feedback strategies.

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  • Improving the accuracy of RNA secondary structure prediction by machine learning based on next-generation sequencing data

    Grant number：16K00404  2016.4 - 2020.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (C)

    Sato Kengo

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    Authorship：Principal investigator 

    Grant amount：\4550000 （ Direct Cost: \3500000 、 Indirect Cost：\1050000 ）

    We have developed a machine learning algorithm that makes it possible to use secondary structure profiles, which are partial structural information, as weak-level learning data, and aims to improve the accuracy of RNA secondary structure prediction without overfitting by learning a large number of secondary structure models that are more precise than existing methods. First, we developed a more robust and accurate method for RNA secondary structure prediction by integrating the free energy minimization method based on the existing Turner thermodynamic model with the machine learning method using a structured SVM. The results of the computer experiments showed that no overfitting was observed, unlike in the existing methods, and the prediction accuracy was improved.

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  • Ultra-fast RNA structural alignments with pseudoknots

    Grant number：25330348  2013.4 - 2017.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (C)

    Sato Kengo

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    Authorship：Principal investigator 

    Grant amount：\4810000 （ Direct Cost: \3700000 、 Indirect Cost：\1110000 ）

    Despite the fact that RNA structural alignments have been studied for a long time, there is still a problem that the computational complexity is still large. For this reason, we cannot perform even a basic analysis of "comparing sequences" by exact methods for relatively long RNA sequences such as long non-coding RNAs and RNA viruses. In this research, we developed a fast and accurate method of calculating RNA structural alignments with consideration of complicated higher order structures such as pseudoknots by a novel algorithm based on maximizing the expected accuracy and the dual decomposition.

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  • Comprehensive analysis of functional non-coding RNAs in cancer genome on multistage carcinogenesis

    Grant number：23241066  2011.4 - 2015.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (A)

    SAKAKIBARA Yasubumi, WAKABAYASHI Yuichi, SATO Kengo

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    Authorship：Coinvestigator(s) 

    Grant amount：\48620000 （ Direct Cost: \37400000 、 Indirect Cost：\11220000 ）

    First, we developed a method for classifying functional RNA combined graph theoretic approach with the software SHARAKU to calculate the mapping shape similarity for processing patterns. The proposed method was applied to the sequence data obtained by the next-generation sequencing for the tumor samples taken in the mouse carcinogenicity experiment, and comprehensive analysis of small derived RNA processed specifically at each stage in multistage carcinogenesis was carried out. Second, we developed a prediction program for protein-RNA interactions. We succeeded in developing a technique for contact prediction between residues-bases. Third, it was carried out verification experiments using transcriptome analysis of carcinogenicity for Meis1 gene of conditional knockout mouse.

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  • Exhaustive prediction of microRNAs, snoRNAs and their targets

    Grant number：22240031  2010.4 - 2013.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (A)

    ASAI Kiyoshi, KIRYU Hisanori, HIROSE Tetsurou, SATO Kengo

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    Authorship：Collaborating Investigator(s) (not designated on Grant-in-Aid) 

    Grant amount：\46410000 （ Direct Cost: \35700000 、 Indirect Cost：\10710000 ）

    The following is the main results of this project. (a) We developed a microRNA finding tool, which combines structural and evolutionary features of microRNAs. We showed that our tool outperforms previous methods with less false positives. (b) We developed an algorithm for computing secondary structural accessibilities of RNA sequences. We were able to detect the evolutionary constraints around the target sites of microRNAs. (c) We developed a tool to calculate the changes of free energies and entropies of RNA secondary structures in response to mutations. We applied our method to mitochondrial tRNAs and showed that the increase of structural fluctuation caused by mutations correlated with disease susceptibility. (d) We developed a tool to predict potential secondary structural contexts for each base of an RNA sequence. We applied our method to binding sites of RNA-binding proteins and detected the structural specificities of the proteins.

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  • Analysis of SNPs that induce structural change of RNA secondary structures

    Grant number：22700305  2010.4 - 2012.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Young Scientists (B)

    SATO Kengo

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    Authorship：Principal investigator 

    Grant amount：\2730000 （ Direct Cost: \2100000 、 Indirect Cost：\630000 ）

    We developed an efficient algorithm for detecting structural changes of RNA secondary structures induced by SNPs on RNA sequences. Comprehensive analysis on the SNP database using our method revealed statistical significance of structural changes induced by synonymous SNPs on coding regions and SNPs on non-coding regions of mRNAs from clinically associated genes.

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  • SYS tern Analysis of Transcriptional Regulation Program

    Grant number：16300095  2004.4 - 2007.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

    SAKAKIBARA Yasubumi, IMOTO Masaya, SATO Kengo, YUGI Katsuyuki

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    Authorship：Coinvestigator(s) 

    Grant amount：\12800000 （ Direct Cost: \12800000 ）

    Context dependencies of transcriptional activations among multiple transcription factors are important to understand the mechanisms of complex transcriptional regulations. Computational identifications of binding sites of transcription factors and computational analyses of cis-elements in promoter regions have been investigated to model cooperative binding activities of multiple transcription factors. We propose a novel discriminative detection method for precisely identifying transcription factor binding sites and their functional variants from both positive and negative samples. Our genome-wide experimental results on yeast Saccharomyces cerevisiae show that our method has presented significant performances for detecting experimentally verified consensus sequences compared with the existing motif detecting methods.
    In addition, we use protein-protein interaction data to infer synergistic binding of cooperative transcription factors. Our fundamental idea is based on the assumption that genes contributing to a similar biological process are regulated under the same control mechanism. Our computational experiments in yeast show that predictions made by our method have successfully identified eight pairs of cooperative transcription factors that have literature evidences but could not be identified by the previous method.

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  • 確率モデルによるWebページ推奨エンジン

    2001

    情報処理振興事業協会  未踏ソフトウェア創造事業  未踏ソフトウェア創造事業

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    Authorship：Principal investigator  Grant type：Competitive

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Social Activities

  • 論文誌Bioinformatics編集委員会, 委員長

    Role(s)： Chief editor

    情報処理学会  IPSJ Transactions on Bioinformatics  2023.4

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  • 論文誌Bioinformatics編集委員会, 副委員長

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    情報処理学会  IPSJ Transactions on Bioinformatics  2021.4 - 2023.3

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  • Genes (Basel), Section Editor

    Role(s)： Editer

    MDPI  Genes  2021

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  • Frontiers in Genetics, Associate Editor

    Role(s)： Editer

    Frontiers  Frontiers in Genetics  2021

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  • 論文誌ジャーナル/JIP編集委員会

    Role(s)： Editer

    情報処理学会  情報処理学会論文誌/JIP  2019.6 - 2023.5

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  • 論文誌Bioinformatics編集委員会

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    情報処理学会  IPSJ Transactions on Bioinformatics  2017.4 - 2021.3

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  • 論文誌Bioinformatics編集委員会

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    情報処理学会  IPSJ Transactions on Bioinformatics  2010.4 - 2014.3

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  • Frontiers in Genetics, Review Editor

    Role(s)： Editer

    Frontiers  Frontiers in Genetics  2010 - 2021

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