PrDOS: prediction of disordered protein regions from amino acid sequence. - Takashi Ishida 002; Kengo Kinoshita (Nucleic Acids Res., 2007)

Prediction of disordered regions in proteins based on the meta approach. - Takashi Ishida 002; Kengo Kinoshita (Bioinform., 2008)

PiSite: a database of protein interaction sites using multiple binding states in the PDB. - Miho Higurashi; Takashi Ishida 002; Kengo Kinoshita (Nucleic Acids Res., 2009)

Fast DNA Sequence Clustering Based on Longest Common Subsequence. - Youhei Namiki; Takashi Ishida 002; Yutaka Akiyama (ICIC (3), 2012)

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 002; Yutaka Akiyama (PRIB, 2012)

An Ultra-Fast Computing Pipeline for Metagenome Analysis with Next-Generation DNA Sequencers. - Shuji Suzuki; Takashi Ishida 002; Yutaka Akiyama (SC Companion, 2012)

Improvement of Protein-Protein Interaction Prediction by Integrating Template-Based and Template-Free Protein Docking. - Masahito Ohue; Yuri Matsuzaki; Takehiro Shimoda; Takashi Ishida 002; Yutaka Akiyama (BCB, 2013)

MEGADOCK-GPU: Acceleration of Protein-Protein Docking Calculation on GPUs. - Takehiro Shimoda; Takashi Ishida 002; Shuji Suzuki; Masahito Ohue; Yutaka Akiyama (BCB, 2013)

The MEGADOCK project: Ultra-high-speed protein-protein interaction prediction tools on supercomputing environments. - Takehiro Shimoda; Masahito Ohue; Yuri Matsuzaki; Takayuki Fujiwara; Nobuyuki Uchikoga; Takashi Ishida 002; Yutaka Akiyama (BCB, 2013)

Acceleration of sequence clustering using longest common subsequence filtering. - Youhei Namiki; Takashi Ishida 002; Yutaka Akiyama (BMC Bioinform., 2013)

D2P2: database of disordered protein predictions. - Matt E. Oates; Pedro Romero; Takashi Ishida 002; Mohamed F. Ghalwash; Marcin J. Mizianty; Bin Xue; Zsuzsanna Dosztányi; Vladimir N. Uversky; Zoran Obradovic; Lukasz A. Kurgan; A. Keith Dunker; Julian Gough (Nucleic Acids Res., 2013)

MEGADOCK 3.0: a high-performance protein-protein interaction prediction software using hybrid parallel computing for petascale supercomputing environments. - Yuri Matsuzaki; Nobuyuki Uchikoga; Masahito Ohue; Takehiro Shimoda; Toshiyuki Sato; Takashi Ishida 002; Yutaka Akiyama (Source Code Biol. Medicine, 2013)

Metagenomic Phylogenetic Classification Using Improved Naïve Bayes. - Yuki Komatsu; Takashi Ishida 002; Yutaka Akiyama (ICIC (3), 2014)

MEGADOCK 4.0: an ultra-high-performance protein-protein docking software for heterogeneous supercomputers. - Masahito Ohue; Takehiro Shimoda; Shuji Suzuki; Yuri Matsuzaki; Takashi Ishida 002; Yutaka Akiyama (Bioinform., 2014)

Extreme Big Data (EBD): Next Generation Big Data Infrastructure Technologies Towards Yottabyte/Year. - Satoshi Matsuoka; Hitoshi Sato; Osamu Tatebe; Michihiro Koibuchi; Ikki Fujiwara; Shuji Suzuki; Masanori Kakuta; Takashi Ishida 002; Yutaka Akiyama; Toyotaro Suzumura; Koji Ueno; Hiroki Kanezashi; et al. (Supercomput. Front. Innov., 2014)

Faster sequence homology searches by clustering subsequences. - Shuji Suzuki; Masanori Kakuta; Takashi Ishida 002; Yutaka Akiyama (Bioinform., 2015)

Protein-protein docking on hardware accelerators: comparison of GPU and MIC architectures. - Takehiro Shimoda; Shuji Suzuki; Masahito Ohue; Takashi Ishida 002; Yutaka Akiyama (BMC Syst. Biol., 2015)

Spresso: an ultrafast compound pre-screening method based on compound decomposition. - Keisuke Yanagisawa; Shunta Komine; Shogo D. Suzuki; Masahito Ohue; Takashi Ishida 002; Yutaka Akiyama (Bioinform., 2017)

Improvement of template-based protein structure prediction by using chimera alignment. - Shuichiro Makigaki; Takashi Ishida 002 (ICBBB, 2018)

Stacking Multiple Molecular Fingerprints for Improving Ligand-Based Virtual Screening. - Yusuke Matsuyama; Takashi Ishida 002 (ICIC (2), 2018)

End-to-End Learning Based Compound Activity Prediction Using Binding Pocket Information. - Toshitaka Tanebe; Takashi Ishida 002 (ICIC (2), 2019)

Sequence alignment using machine learning for accurate template-based protein structure prediction. - Shuichiro Makigaki; Takashi Ishida 002 (Bioinform., 2020)

Development of Computational Pipeline Software for Genome/Exome Analysis on the K Computer. - Kento Aoyama; Masanori Kakuta; Yuri Matsuzaki; Takashi Ishida 002; Masahito Ohue; Yutaka Akiyama (Supercomput. Front. Innov., 2020)

End-to-end learning for compound activity prediction based on binding pocket information. - Toshitaka Tanebe; Takashi Ishida 002 (BMC Bioinform., 2021)

Single-Step Retrosynthesis Prediction Based on the Identification of Potential Disconnection Sites Using Molecular Substructure Fingerprints. - Haris Hasic; Takashi Ishida 002 (J. Chem. Inf. Model., 2021)

Helix encoder: a compound-protein interaction prediction model specifically designed for class A GPCRs. - Haruki Yamane; Takashi Ishida 002 (Frontiers Bioinform., 2023)

Comparing Supervised Learning and Rigorous Approach for Predicting Protein Stability upon Point Mutations in Difficult Targets. - Jason Kurniawan; Takashi Ishida 002 (J. Chem. Inf. Model., 2023)