Updated on 2026/03/11

写真a

 
ISHIDA TAKASHI
 
Organization
School of Computing Professor
Title
Professor
External link

Research Areas

  • Informatics / Life, health and medical informatics

Papers

  • Multimodal Artificial Intelligence for Predicting 3- and 5-Year Risks of Myopic Choroidal Neovascularization in High Myopia

    Yining Wang, Takashi Ishida, Ziye Wang, Yijin Wu, Koju Kamoi, Daniel Shu Wei Ting, Kyoko Ohno-Matsui

    Ophthalmology Retina   2026.2

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.oret.2026.02.003

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  • Enhancing Interpretability of Survival Models With High Predictive Performance: Integrating Cox Proportional Hazards With Machine Learning Methods

    Shijie Chen, Takashi Ishida

    IEEE Access   2026

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1109/ACCESS.2026.3661629

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  • Prediction of large conformational changes of a protein binding pocket associated with ligand binding

    kanta sakai, Takashi Ishida

    2024.3

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    <jats:p>Docking simulation, a key technique in virtual screening, typically treats proteins as rigid bodies. However, proteins are inherently flexible, and ligand binding can induce significant conformational changes, affecting prediction accuracy. This study proposes a new approach to identify protein binding pockets that exhibit substantial conformational changes upon lig-and binding, potentially impacting docking simulation accuracy. In this research, we developed a prediction model using graph neural network to identify protein pockets with large conformational changes. To train the model, we constructed a dataset by calculating conformational changes in ligand-binding sites between multiple holo structures corresponding to the apo structure. We evaluated the performance of the prediction model and the results demonstrated that our model could iden-tify proteins with significant conformational changes, although the prediction accuracy remains low, with an AUC of 0.58 on the test data. This study highlights the potential of deep learning approaches in addressing the challenges of protein flexi-bility in virtual screening and docking simulations.</jats:p>

    DOI: 10.26434/chemrxiv-2024-tp6nl

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  • Comparing Supervised Learning and Rigorous Approach for Predicting Protein Stability upon Point Mutations in Difficult Targets Reviewed International journal

    Jason Kurniawan, Takashi Ishida

    Journal of Chemical Information and Modeling   2023.11

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    Authorship:Corresponding author   Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acs.jcim.3c00750

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  • Discovery of a Hidden Trypanosoma cruzi Spermidine Synthase Binding Site and Inhibitors through In Silico, In Vitro, and X-ray Crystallography Reviewed International journal

    Ryunosuke Yoshino, Nobuaki Yasuo, Yohsuke Hagiwara, Takashi Ishida, Daniel Ken Inaoka, Yasushi Amano, Yukihiro Tateishi, Kazuki Ohno, Ichiji Namatame, Tatsuya Niimi, Masaya Orita, Kiyoshi Kita, Yutaka Akiyama, Masakazu Sekijima

    ACS Omega   2023.7

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    Authorship:Corresponding author   Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acsomega.3c01314

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  • Helix encoder: a compound-protein interaction prediction model specifically designed for class A GPCRs Reviewed International journal

    Haruki Yamane, Takashi Ishida

    Frontiers in Bioinformatics   2023.5

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    Authorship:Corresponding author   Publishing type:Research paper (scientific journal)  

    <jats:p>Class A G protein-coupled receptors (GPCRs) represent the largest class of GPCRs. They are essential targets of drug discovery and thus various computational approaches have been applied to predict their ligands. However, there are a large number of orphan receptors in class A GPCRs and it is difficult to use a general protein-specific supervised prediction scheme. Therefore, the compound-protein interaction (CPI) prediction approach has been considered one of the most suitable for class A GPCRs. However, the accuracy of CPI prediction is still insufficient. The current CPI prediction model generally employs the whole protein sequence as the input because it is difficult to identify the important regions in general proteins. In contrast, it is well-known that only a few transmembrane helices of class A GPCRs play a critical role in ligand binding. Therefore, using such domain knowledge, the CPI prediction performance could be improved by developing an encoding method that is specifically designed for this family. In this study, we developed a protein sequence encoder called the Helix encoder, which takes only a protein sequence of transmembrane regions of class A GPCRs as input. The performance evaluation showed that the proposed model achieved a higher prediction accuracy compared to a prediction model using the entire protein sequence. Additionally, our analysis indicated that several extracellular loops are also important for the prediction as mentioned in several biological researches.</jats:p>

    DOI: 10.3389/fbinf.2023.1193025

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  • Protein Model Quality Estimation Using Molecular Dynamics Simulation Reviewed International journal

    Jason Kurniawan, Takashi Ishida

    ACS Omega   2022.7

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    Authorship:Corresponding author   Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acsomega.2c01475

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  • How to select the best model from AlphaFold2 structures?

    Yuma Takei, Takashi Ishida

    2022.4

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    Authorship:Corresponding author   Publishing type:Research paper (scientific journal)   Publisher:Cold Spring Harbor Laboratory  

    DOI: 10.1101/2022.04.05.487218

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  • A Benchmark Dataset for Evaluating Practical Performance of Model Quality Assessment of Homology Models Reviewed International journal

    Yuma Takei, Takashi Ishida

    Bioengineering   9 ( 3 )   118 - 118   2022.3

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    Authorship:Corresponding author   Publishing type:Research paper (scientific journal)   Publisher:{MDPI} {AG}  

    DOI: 10.3390/bioengineering9030118

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  • Modeling SARS‐CoV‐2 proteins in the CASP‐commons experiment Reviewed International coauthorship International journal

    Andriy Kryshtafovych, John Moult, Wendy M. Billings, Dennis Della Corte, Krzysztof Fidelis, Sohee Kwon, Kliment Olechnovič, Chaok Seok, Česlovas Venclovas, Jonghun Won, CASP‐COVID participants

    Proteins: Structure, Function, and Bioinformatics   2021.12

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

    <jats:title>Abstract</jats:title><jats:p>Critical Assessment of Structure Prediction (CASP) is an organization aimed at advancing the state of the art in computing protein structure from sequence. In the spring of 2020, CASP launched a community project to compute the structures of the most structurally challenging proteins coded for in the SARS‐CoV‐2 genome. Forty‐seven research groups submitted over 3000 three‐dimensional models and 700 sets of accuracy estimates on 10 proteins. The resulting models were released to the public. CASP community members also worked together to provide estimates of local and global accuracy and identify structure‐based domain boundaries for some proteins. Subsequently, two of these structures (ORF3a and ORF8) have been solved experimentally, allowing assessment of both model quality and the accuracy estimates. Models from the AlphaFold2 group were found to have good agreement with the experimental structures, with main chain GDT_TS accuracy scores ranging from 63 (a correct topology) to 87 (competitive with experiment).</jats:p>

    DOI: 10.1002/prot.26231

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  • Computer aided drug discovery review for infectious diseases with case study of anti-Chagas project Reviewed International journal

    Nobuaki Yasuo, Takashi Ishida, Masakazu Sekijima

    Parasitology International   2021.8

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

    DOI: 10.1016/j.parint.2021.102366

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  • Taxonomic and Gene Category Analyses of Subgingival Plaques from a Group of Japanese Individuals with and without Periodontitis Reviewed International journal

    Kazuki Izawa, Kazuko Okamoto-Shibayama, Daichi Kita, Sachiyo Tomita, Atsushi Saito, Takashi Ishida, Masahito Ohue, Yutaka Akiyama, Kazuyuki Ishihara

    International Journal of Molecular Sciences   22 ( 10 )   5298 - 5298   2021.5

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    Publishing type:Research paper (scientific journal)   Publisher:{MDPI} {AG}  

    DOI: 10.3390/ijms22105298

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  • End-to-end learning for compound activity prediction based on binding pocket information Reviewed International coauthorship International journal

    Toshitaka Tanebe, Takashi Ishida

    BMC Bioinformatics   2021.5

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    Authorship:Corresponding author   Publishing type:Research paper (scientific journal)  

    DOI: 10.1186/s12859-021-04440-w

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  • P3CMQA: Single-Model Quality Assessment Using 3DCNN with Profile-Based Features Reviewed International journal

    Yuma Takei, Takashi Ishida

    Bioengineering   8 ( 3 )   40 - 40   2021.3

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    Authorship:Corresponding author   Publishing type:Research paper (scientific journal)   Publisher:{MDPI} {AG}  

    DOI: 10.3390/bioengineering8030040

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  • Single-Step Retrosynthesis Prediction Based on the Identification of Potential Disconnection Sites Using Molecular Substructure Fingerprints Reviewed International journal

    Haris Hasic, Takashi Ishida

    Journal of Chemical Information and Modeling   61 ( 2 )   641 - 652   2021.2

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    Authorship:Corresponding author   Publishing type:Research paper (scientific journal)   Publisher:American Chemical Society ({ACS})  

    DOI: 10.1021/acs.jcim.0c01100

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  • Mathematical proof of the third order accuracy of the speedy double bootstrap method Reviewed International coauthorship International journal

    Aizhen Ren, Takashi Ishida, Yutaka Akiyama

    Communications in Statistics - Theory and Methods   2020.8

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

    DOI: 10.1080/03610926.2019.1594295

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  • Development of Computational Pipeline Software for Genome/Exome Analysis on the K Computer

    Takashi Ishida

    Supercomputing Frontiers and Innovations   2020.3

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.14529/jsfi200102

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  • Sequence alignment using machine learning for accurate template-based protein structure prediction Reviewed International journal

    Shuichiro Makigaki, Takashi Ishida

    Bioinformatics   2020.1

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

    <jats:title>Abstract</jats:title>
    <jats:sec>
    <jats:title>Motivation</jats:title>
    <jats:p>Template-based modeling, the process of predicting the tertiary structure of a protein by using homologous protein structures, is useful if good templates can be found. Although modern homology detection methods can find remote homologs with high sensitivity, the accuracy of template-based models generated from homology-detection-based alignments is often lower than that from ideal alignments.</jats:p>
    </jats:sec>
    <jats:sec>
    <jats:title>Results</jats:title>
    <jats:p>In this study, we propose a new method that generates pairwise sequence alignments for more accurate template-based modeling. The proposed method trains a machine learning model using the structural alignment of known homologs. It is difficult to directly predict sequence alignments using machine learning. Thus, when calculating sequence alignments, instead of a fixed substitution matrix, this method dynamically predicts a substitution score from the trained model. We evaluate our method by carefully splitting the training and test datasets and comparing the predicted structure’s accuracy with that of state-of-the-art methods. Our method generates more accurate tertiary structure models than those produced from alignments obtained by other methods.</jats:p>
    </jats:sec>
    <jats:sec>
    <jats:title>Availability and implementation</jats:title>
    <jats:p>https://github.com/shuichiro-makigaki/exmachina.</jats:p>
    </jats:sec>
    <jats:sec>
    <jats:title>Supplementary information</jats:title>
    <jats:p>Supplementary data are available at Bioinformatics online.</jats:p>
    </jats:sec>

    DOI: 10.1093/bioinformatics/btz483

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  • Sequence alignment generation using intermediate sequence search for homology modeling Reviewed International journal

    Shuichiro Makigaki, Takashi Ishida

    Computational and Structural Biotechnology Journal   2020

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

    DOI: 10.1016/j.csbj.2020.07.012

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  • A prospective compound screening contest identified broader inhibitors for Sirtuin 1. Reviewed

    Chiba S, Ohue M, Gryniukova A, Borysko P, Zozulya S, Yasuo N, Yoshino R, Ikeda K, Shin WH, Kihara D, Iwadate M, Umeyama H, Ichikawa T, Teramoto R, Hsin KY, Gupta V, Kitano H, Sakamoto M, Higuchi A, Miura N, Yura K, Mochizuki M, Ramakrishnan C, Thangakani AM, Velmurugan D, Gromiha MM, Nakane I, Uchida N, Hakariya H, Tan M, Nakamura HK, Suzuki SD, Ito T, Kawatani M, Kudoh K, Takashina S, Yamamoto KZ, Moriwaki Y, Oda K, Kobayashi D, Okuno T, Minami S, Chikenji G, Prathipati P, Nagao C, Mohsen A, Ito M, Mizuguchi K, Honma T, Ishida T, Hirokawa T, Akiyama Y, Sekijima M

    Scientific reports   9 ( 1 )   19585   2019.12

  • Protein model accuracy estimation based on local structure quality assessment using 3D convolutional neural network Reviewed International journal

    Rin Sato, Takashi Ishida

    PLOS ONE   14 ( 9 )   2019.9

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    Authorship:Corresponding author   Publishing type:Research paper (scientific journal)   Publisher:Public Library of Science ({PLoS})  

    DOI: 10.1371/journal.pone.0221347

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  • An iterative compound screening contest method for identifying target protein inhibitors using the tyrosine-protein kinase Yes Reviewed

    Shuntaro Chiba, Takashi Ishida, Kazuyoshi Ikeda, Masahiro Mochizuki, Reiji Teramoto, Y-h. Taguchi, Mitsuo Iwadate, Hideaki Umeyama, Chandrasekaran Ramakrishnan, A. Mary Thangakani, D. Velmurugan, M. Michael Gromiha, Tatsuya Okuno, Koya Kato, Shintaro Minami, George Chikenji, Shogo D. Suzuki, Keisuke Yanagisawa, Woong-Hee Shin, Daisuke Kihara, Kazuki Z. Yamamoto, Yoshitaka Moriwaki, Nobuaki Yasuo, Ryunosuke Yoshino, Sergey Zozulya, Petro Borysko, Roman Stavniichuk, Teruki Honma, Takatsugu Hirokawa, Yutaka Akiyama, Masakazu Sekijima

    SCIENTIFIC REPORTS   7 ( 12038 )   1 - 13   2017.9

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

    DOI: 10.1038/s41598-017-10275-4

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  • In silico, in vitro, X-ray crystallography, and integrated strategies for discovering spermidine synthase inhibitors for Chagas disease Reviewed

    Ryunosuke Yoshino, Nobuaki Yasuo, Yohsuke Hagiwara, Takashi Ishida, Daniel Ken Inaoka, Yasushi Amano, Yukihiro Tateishi, Kazuki Ohno, Ichiji Namatame, Tatsuya Niimi, Masaya Orita, Kiyoshi Kita, Yutaka Akiyama, Masakazu Sekijima

    SCIENTIFIC REPORTS   7 ( 1 )   6666   2017.7

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

    DOI: 10.1038/s41598-017-06411-9

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  • Spresso: an ultrafast compound pre-screening method based on compound decomposition

    Keisuke Yanagisawa, Shunta Komine, Shogo D Suzuki, Masahito Ohue, Takashi Ishida, Yutaka Akiyama

    Bioinformatics   33 ( 23 )   3836 - 3843   2017.3

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

    Abstract

    Motivation

    Recently, the number of available protein tertiary structures and compounds has increased. However, structure-based virtual screening is computationally expensive owing to docking simulations. Thus, methods that filter out obviously unnecessary compounds prior to computationally expensive docking simulations have been proposed. However, the calculation speed of these methods is not fast enough to evaluate ≥ 10 million compounds.

    Results

    In this article, we propose a novel, docking-based pre-screening protocol named Spresso (Speedy PRE-Screening method with Segmented cOmpounds). Partial structures (fragments) are common among many compounds; therefore, the number of fragment variations needed for evaluation is smaller than that of compounds. Our method increases calculation speeds by ∼200-fold compared to conventional methods.

    Availability and Implementation

    Spresso is written in C ++ and Python, and is available as an open-source code (http://www.bi.cs.titech.ac.jp/spresso/) under the GPLv3 license.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

    DOI: 10.1093/bioinformatics/btx178

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  • GPU-acceleration of sequence homology searches with database subsequence clustering

    S Suzuki, M Kakuta, T Ishida, Y Akiyama

    PLoS one   2016

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  • Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target. Reviewed

    Chiba S, Ikeda K, Ishida T, Gromiha MM, Taguchi YH, Iwadate M, Umeyama H, Hsin KY, Kitano H, Yamamoto K, Sugaya N, Kato K, Okuno T, Chikenji G, Mochizuki M, Yasuo N, Yoshino R, Yanagisawa K, Ban T, Teramoto R, Ramakrishnan C, Thangakani AM, Velmurugan D, Prathipati P, Ito J, Tsuchiya Y, Mizuguchi K, Honma T, Hirokawa T, Akiyama Y, Sekijima M

    Scientific reports   5   17209   2015.11

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1038/srep17209

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  • Protein-protein docking on hardware accelerators: comparison of GPU and MIC architectures

    Shimoda, Takehiro, Suzuki, Shuji, Ohue, Masahito, Ishida, Takashi, Akiyama, Yutaka

    Bmc Systems Biology   9   2015

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1186/1752-0509-9-S1-S6

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  • Faster sequence homology searches by clustering subsequences

    Suzuki, Shuji, Kakuta, Masanori, Ishida, Takashi, Akiyama, Yutaka

    Bioinformatics   31 ( 8 )   2015

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1093/bioinformatics/btu780

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  • Drug Clearance Pathway Prediction Based on Semi-supervised Learning

    Yanagisawa, Keisuke, Ishida, Takashi, Akiyama, Yutaka

    IPSJ Transactions on Bioinformatics   8   21 - 27   2015

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    Publishing type:Research paper (scientific journal)   Publisher:Information Processing Society of Japan  

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  • MEGADOCK 4.0: an ultra-high-performance protein-protein docking software for heterogeneous supercomputers

    Ohue, Masahito, Shimoda, Takehiro, Suzuki, Shuji, Matsuzaki, Yuri, Ishida, Takashi, Akiyama, Yutaka

    Bioinformatics   30 ( 22 )   2014

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1093/bioinformatics/btu532

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  • GHOSTX: An Improved Sequence Homology Search Algorithm Using a Query Suffix Array and a Database Suffix Array

    Suzuki, Shuji, Kakuta, Masanori, Ishida, Takashi, Akiyama, Yutaka

    Plos One   9 ( 8 )   2014

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1371/journal.pone.0103833

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  • MEGADOCK: An All-to-All Protein-Protein Interaction Prediction System Using Tertiary Structure Data

    Ohue, Masahito, Matsuzaki, Yuri, Uchikoga, Nobuyuki, Ishida, Takashi, Akiyama, Yutaka

    Protein and Peptide Letters   21 ( 8 )   2014

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  • (DP2)-P-2: database of disordered protein predictions

    Oates, Matt E., Romero, Pedro, Ishida, Takashi, Ghalwash, Mohamed, Mizianty, Marcin J., Xue, Bin, Dosztanyi, Zsuzsanna, Uversky, Vladimir N., Obradovic, Zoran, Kurgan, Lukasz, Dunker, A. Keith, Gough, Julian

    Nucleic Acids Research   41 ( D1 )   2013

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1093/nar/gks1226

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  • Assessing statistical reliability of phylogenetic trees via a speedy double bootstrap method

    Ren, Aizhen, Ishida, Takashi, Akiyama, Yutaka

    Molecular Phylogenetics and Evolution   67 ( 2 )   2013

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.ympev.2013.02.011

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  • Acceleration of sequence clustering using longest common subsequence filtering

    Namiki, Youhei, Ishida, Takashi, Akiyama, Yutaka

    Bmc Bioinformatics   14   2013

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1186/1471-2105-14-S8-S7

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  • Improvement of Protein-Protein Interaction Prediction by Integrating Template-Based and Template-Free Protein Docking.

    Masahito Ohue, Yuri Matsuzaki, Takehiro Shimoda, Takashi Ishida 0002, Yutaka Akiyama

    ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics. ACM-BCB 2013(BCB)   666 - 666   2013

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    Publishing type:Research paper (international conference proceedings)   Publisher:ACM  

    DOI: 10.1145/2506583.2506669

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    Other Link: https://dblp.uni-trier.de/db/conf/bcb/bcb2013.html#OhueMSIA13

  • An ultra-fast computing pipeline for metagenome analysis with next-generation DNA sequencers

    Suzuki, Shuji, Ishida, Takashi, Akiyama, Yutaka, IEEE

    2012 Sc Companion: High Performance Computing, Networking, Storage and Analysis (Scc)   2012

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  • Improvement of the Protein-Protein Docking Prediction by Introducing a Simple Hydrophobic Interaction Model: An Application to Interaction Pathway Analysis.

    Masahito Ohue, Yuri Matsuzaki, Takashi Ishida 0002, Yutaka Akiyama

    Pattern Recognition in Bioinformatics - 7th IAPR International Conference(PRIB)   178 - 187   2012

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    Publishing type:Research paper (international conference proceedings)   Publisher:Springer  

    DOI: 10.1007/978-3-642-34123-6_16

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    Other Link: https://dblp.uni-trier.de/db/conf/prib/prib2012.html#OhueMIA12

  • Fast DNA Sequence Clustering Based on Longest Common Subsequence.

    Youhei Namiki, Takashi Ishida 0002, Yutaka Akiyama

    Emerging Intelligent Computing Technology and Applications - 8th International Conference   453 - 460   2012

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    Publishing type:Research paper (international conference proceedings)   Publisher:Springer  

    DOI: 10.1007/978-3-642-31837-5_66

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    Other Link: https://dblp.uni-trier.de/db/conf/icic/icic2012-3.html#NamikiIA12

  • Absolute quality evaluation of protein model structures using statistical potentials with respect to the native and reference states

    Shirota, Matsuyuki, Ishida, Takashi, Kinoshita, Kengo

    Proteins-Structure Function and Bioinformatics   79 ( 5 )   2011

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1002/prot.22982

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  • Community-Wide Assessment of Protein-Interface Modeling Suggests Improvements to Design Methodology

    Fleishman, Sarel J., Whitehead, Timothy A., Strauch, Eva-Maria, Corn, Jacob E., Qin, Sanbo, Zhou, Huan-Xiang, Mitchell, Julie C., Demerdash, Omar N. A., Takeda-Shitaka, Mayuko, Terashi, Genki, Moal, Iain H., Li, Xiaofan, Bates, Paul A., Zacharias, Martin, Park, Hahnbeom, Ko, Jun-su, Lee, Hasup, Seok, Chaok, Bourquard, Thomas, Bernauer, Julie, Poupon, Anne, Aze, Jerome, Soner, Seren, Ovali, Sefik Kerem, Ozbek, Pemra, Ben Tal, Nir, Haliloglu, Turkan, Hwang, Howook, Vreven, Thom, Pierce, Brian G., Weng, Zhiping, Perez-Cano, Laura, Pons, Caries, Fernandez-Recio, Juan, Jiang, Fan, Yang, Feng, Gong, Xinqi, Cao, Libin, Xu, Xianjin, Liu, Bin, Wang, Panwen, Li, Chunhua, Wang, Cunxin, Rober, Charles H., Guharoy, Mainak, Liu, Shiyong, Huang, Yangyu, Li, Lin, Guo, Dachuan, Chen, Ying, Xiao, Yi, London, Nir, Itzhaki, Zohar, Schueler-Furman, Ora, Inbar, Yuval, Potapov, Vladimir, Cohen, Mati, Schreiber, Gideon, Tsuchiya, Yuko, Kanamori, Eiji, Standley, Daron M., Nakamura, Haruki, Kinoshita, Kengo, Driggers, Camden M., Hall, Robert G., Morgan, Jessica L., Hsu, Victor L., Zhan, Jian, Yang, Yuedong, Zhou, Yaoqi, Kastritis, Panagiotis L., Bonvin, Alexandre M. J. J., Zhang, Weiyi, Camacho, Carlos J., Kilambi, Krishna P., Sircar, Aroop, Gray, Jeffrey J., Ohue, Masahito, Uchikoga, Nobuyuki, Matsuzaki, Yuri, Ishida, Takashi, Akiyama, Yutaka, Khashan, Raed, Bush, Stephen, Fouches, Denis, Tropsha, Alexander, Esquivel-Rodriguez, Juan, Kihara, Daisuke, Stranges, P. Benjamin, Jacak, Ron, Kuhlman, Brian, Huang, Sheng-You, Zou, Xiaoqin, Wodak, Shoshana J., Janin, Joel, Baker, David

    Journal of Molecular Biology   414 ( 2 )   2011

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    DOI: 10.1016/j.jmb.2011.09.031

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  • Analyses on hydrophobicity and attractiveness of all-atom distance-dependent potentials

    Shirota, Matsuyuki, Ishida, Takashi, Kinoshita, Kengo

    Protein Science   18 ( 9 )   2009

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    DOI: 10.1002/pro.201

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  • PiSite: a database of protein interaction sites using multiple binding states in the PDB

    Higurashi, Miho, Ishida, Takashi, Kinoshita, Kengo

    Nucleic Acids Research   37   2009

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    DOI: 10.1093/nar/gkn659

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  • Identification of transient hub proteins and the possible structural basis for their multiple interactions

    Higurashi, Miho, Ishida, Takashi, Kinoshita, Kengo

    Protein Science   17 ( 1 )   2008

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1110/ps.073196308

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  • Prediction of disordered regions in proteins based on the meta approach

    Ishida, Takashi, Kinoshita, Kengo

    Bioinformatics   24 ( 11 )   2008

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1093/bioinformatics/btn195

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  • PrDOS: prediction of disordered protein regions from amino acid sequence

    Ishida, Takashi, Kinoshita, Kengo

    Nucleic Acids Research   35   2007

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    DOI: 10.1093/nar/gkm363

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  • Potential for assessing quality of protein structure based on contact number prediction

    Ishida, Takashi, Nakamura, Shugo, Shimizu, Kentaro

    Proteins-Structure Function and Bioinformatics   64 ( 4 )   2006

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    DOI: 10.1002/prot.21047

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  • Aggregation mechanism of polyglutamine diseases revealed using quantum chemical calculations, fragment molecular orbital calculations, molecular dynamics simulations, and binding free energy calculations

    Tsukamoto, Koki, Shimizu, Hideaki, Ishida, Takashi, Akiyama, Yutaka, Nukina, Nobuyuki

    Journal of Molecular Structure-Theochem   778 ( 1-3 )   2006

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    DOI: 10.1016/j.theochem.2006.08.046

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  • Development of an ab initio protein structure prediction system ABLE.

    Ishida, Takashi, Nishimura, Takeshi, Nozaki, Makoto, Inoue, Tsuyoshi, Terada, Tohru, Nakamura, Shugo, Shimizu, Kentaro

    Genome informatics. International Conference on Genome Informatics   14   2003

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  • フラグメント分割に基づく超高速化合物プレスクリーニング手法ESPRESSO—ESPRESSO : An ultrafast compound pre-screening method based on compound decomposition

    柳澤 渓甫, 小峰 駿汰, 鈴木 翔吾, 大上 雅史, 石田 貴士, 秋山 泰

    電子情報通信学会技術研究報告 = IEICE technical report : 信学技報   116 ( 120 )   99 - 105   2016.7

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    Other Link: https://ndlsearch.ndl.go.jp/books/R000000004-I027541187

  • Drug clearance pathway prediction using semi-supervised learning

    Keisuke Yanagisawa, Takashi Ishida, Yutaka Akiyama

    IPSJ SIG Notes   2014 ( 10 )   1 - 6   2014.6

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    Nowadays, drug development requires too much time and budget, and it is necessary to reduce them. In order to accept a compound as a new drug, it must be confirmed that it is metabolized and excreted. In this respect, one of the computational methods used for selecting compounds is drug clearance pathway prediction. This prediction method uses well-known drug's clearance pathway data as a training set. However data is expensive to get, and thus there are too few data. For this reason, we evaluated the usefulness of semi-supervised learning in this prediction problem, and tried to improve accuracy of this clearance pathway prediction. We also tried to add some features of compounds which are selected from 802 features by greedy algorithm to improve accuracy and evaluated their effect.

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  • Drug clearance pathway prediction using semi-supervised learning

    Keisuke Yanagisawa, Takashi Ishida, Yutaka Akiyama

    IPSJ SIG technical reports   2014 ( 10 )   1 - 6   2014.6

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    Nowadays, drug development requires too much time and budget, and it is necessary to reduce them. In order to accept a compound as a new drug, it must be confirmed that it is metabolized and excreted. In this respect, one of the computational methods used for selecting compounds is drug clearance pathway prediction. This prediction method uses well-known drug's clearance pathway data as a training set. However data is expensive to get, and thus there are too few data. For this reason, we evaluated the usefulness of semi-supervised learning in this prediction problem, and tried to improve accuracy of this clearance pathway prediction. We also tried to add some features of compounds which are selected from 802 features by greedy algorithm to improve accuracy and evaluated their effect.

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  • 半教師付き学習を用いた薬物クリアランス経路予測—Drug clearance pathway prediction using semi-supervised learning

    柳澤 渓甫, 石田 貴士, 秋山 泰

    電子情報通信学会技術研究報告 = IEICE technical report : 信学技報   114 ( 104 )   55 - 60   2014.6

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    Other Link: https://ndlsearch.ndl.go.jp/books/R000000004-I025589621

  • Memory cost reduction of Velvet by dividing de Bruijn Graphs

    Norikazu Sugiura, Takashi Ishida, Yutaka Akiyama, Masakazu Sekijima

    IPSJ SIG Notes   2013 ( 6 )   1 - 7   2013.12

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    It is a well-known fact that the memory consumption of Velvet, which is one of the representative de novo assembler based on de Bruijn Graph, is too large. Velvet is composed of two steps, and several methods have been already proposed for decreasing the memory consumption of the first step by dividing the hash table. Here we proposed a graph dividing method. By using this method, we have succeeded to decrease the memory consumption of the latter step of Velvet.

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  • Current Status of in Silico Drug Discovery for Neglected Tropical Diseases

    ISHIDA Takashi, AKIYAMA Yutaka, SEKIJIMA Masakazu, OHNO Kazuki, ORITA Masaya

    The quarterly journal of Ritsumeikan University   52 ( 2 )   267 - 282   2013.11

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    DOI: 10.34382/00001082

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    Other Link: http://hdl.handle.net/10367/5157

  • Improvement of drug clearance pathway prediction using binding free energy calculation

    SAITO YUKI, ISHIDA TAKASHI, SEKIJIMA MASAKAZU, AKIYAMA YUTAKA

    IEICE technical report. Neurocomputing   113 ( 111 )   83 - 88   2013.6

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    Recently, drug development needs longer term and larger budget than before, so it is needed to decrease of them. From such a reason, the computer simulation-based techniques to design drugs which inhibit a target-protein is in the spotlight now. On the other hand, the drugs must be metabolized and excreted. And, one of the techniques to search the chemical compound which satisfy this condition is the drug clearance pathway prediction. Then, we tried to use binding free energy calculation of protein-ligand complex with the docking calculation, one of the computer simulation, for the drug clearance pathway prediction, we improved precision of the system for drug clearance pathway prediction previously developed in our lab.

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  • Evaluation of Sparse k-mer graph algorithm and its implementation on Velvet

    YOSHIKAWA SHUNSUKE, ISHIDA TAKASHI, SEKIJIMA MASAKAZU, AKIYAMA YUTAKA

    IEICE technical report. Neurocomputing   113 ( 111 )   1 - 7   2013.6

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    Development of Next Generation Sequencing technologies has provided an unprecedented increase in DNA sequencing throuput while this technology produces shorter reads than previous technology. These two factors make a number of new short read de novo assemblers. In this report, by evaluation of Sparse Assembler, we clarified that this asssembler is useful at the point of memory usage and time, while producing less quality of contigs than other assemblers already developed. This study was made to assembly with less computer memory and in shorter time than previous study by implement Sparse k-mer graph algorithm on Velvet, which is the most widely used assembler.

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  • Characterizing Delaunay Graphs via Fixed Point Theorem

    2012 ( 24 )   1 - 3   2012.11

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  • Inprovement of Velvet by dividing PreGraph

    2012 ( 24 )   1 - 3   2012.11

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  • De novo sequence assembly based on divided hash tables

    SUGIURA NORIKAZU, ISHIDA TAKASHI, SEKIJIMA MASAKAZU, AKIYAMA YUTAKA

    IEICE technical report. Neurocomputing   112 ( 108 )   133 - 139   2012.6

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    Velvet is one of the most representative de novo assembler. However it has a problem that its memory consumption is too large for large scale assembling. Here, we propose a method to decrease the memory consumption of velveth which is the first half of Velvet and requires generally larger memory than the remaining half part. We propose a novel hash dividing method by dividing reads. By using this method, we have succeeded to decrease the elapsed time compared to the existing method, which divides a hash table corresponding the k-mer value.

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  • De novo sequence assembly based on divided hash tables

    2012 ( 25 )   1 - 7   2012.6

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