Extraction of correlated gene clusters from multiple genomic data by generalized kernel canonical correlation analysis. - Yoshihiro Yamanishi; Jean-Philippe Vert; Akihiro Nakaya; Minoru Kanehisa (ISMB (Supplement of Bioinformatics), 2003)

Protein network inference from multiple genomic data: a supervised approach. - Yoshihiro Yamanishi; Jean-Philippe Vert; Minoru Kanehisa (ISMB/ECCB (Supplement of Bioinformatics), 2004)

Supervised Graph Inference. - Jean-Philippe Vert; Yoshihiro Yamanishi (NIPS, 2004)

Supervised enzyme network inference from the integration of genomic data and chemical information. - Yoshihiro Yamanishi; Jean-Philippe Vert; Minoru Kanehisa (ISMB (Supplement of Bioinformatics), 2005)

The inference of protein-protein interactions by co-evolutionary analysis is improved by excluding the information about the phylogenetic relationships. - Tetsuya Sato 003; Yoshihiro Yamanishi; Minoru Kanehisa; Hiroyuki Toh (Bioinform., 2005)

Sensitivity analysis in functional principal component analysis. - Yoshihiro Yamanishi; Yutaka Tanaka (Comput. Stat., 2005)

Partial correlation coefficient between distance matrices as a new indicator of protein-protein interactions. - Tetsuya Sato 003; Yoshihiro Yamanishi; Katsuhisa Horimoto; Minoru Kanehisa; Hiroyuki Toh (Bioinform., 2006)

Inference of Protein-Protein Interactions by Using Co-evolutionary Information. - Tetsuya Sato 003; Yoshihiro Yamanishi; Katsuhisa Horimoto; Minoru Kanehisa; Hiroyuki Toh (AB, 2007)

Glycan classification with tree kernels. - Yoshihiro Yamanishi; Francis R. Bach; Jean-Philippe Vert (Bioinform., 2007)

Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. - Yoshihiro Yamanishi; Michihiro Araki; Alex Gutteridge; Wataru Honda; Minoru Kanehisa (ISMB, 2008)

Supervised Bipartite Graph Inference. - Yoshihiro Yamanishi (NIPS, 2008)

KEGG for linking genomes to life and the environment. - Minoru Kanehisa; Michihiro Araki; Susumu Goto; Masahiro Hattori; Mika Hirakawa; Masumi Itoh; Toshiaki Katayama; Shuichi Kawashima; Shujiro Okuda; Toshiaki Tokimatsu; Yoshihiro Yamanishi (Nucleic Acids Res., 2008)

On Pairwise Kernels: An Efficient Alternative and Generalization Analysis. - Hisashi Kashima; Satoshi Oyama; Yoshihiro Yamanishi; Koji Tsuda (PAKDD, 2009)

Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction. - Hisashi Kashima; Tsuyoshi Kato; Yoshihiro Yamanishi; Masashi Sugiyama; Koji Tsuda (SDM, 2009)

Supervised prediction of drug-target interactions using bipartite local models. - Kevin Bleakley; Yoshihiro Yamanishi (Bioinform., 2009)

Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: a semi-supervised approach. - Hisashi Kashima; Yoshihiro Yamanishi; Tsuyoshi Kato; Masashi Sugiyama; Koji Tsuda (Bioinform., 2009)

E-zyme: predicting potential EC numbers from the chemical transformation pattern of substrate-product pairs. - Yoshihiro Yamanishi; Masahiro Hattori; Masaaki Kotera; Susumu Goto; Minoru Kanehisa (Bioinform., 2009)

Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. - Yoshihiro Yamanishi; Masaaki Kotera; Minoru Kanehisa; Susumu Goto (Bioinform., 2010)

Cartesian Kernel: An Efficient Alternative to the Pairwise Kernel. - Hisashi Kashima; Satoshi Oyama; Yoshihiro Yamanishi; Koji Tsuda (IEICE Trans. Inf. Syst., 2010)

Predicting drug side-effect profiles: a chemical fragment-based approach. - Edouard Pauwels; Véronique Stoven; Yoshihiro Yamanishi (BMC Bioinform., 2011)

Extracting Sets of Chemical Substructures and Protein Domains Governing Drug-Target Interactions. - Yoshihiro Yamanishi; Edouard Pauwels; Hiroto Saigo; Véronique Stoven (J. Chem. Inf. Model., 2011)

Relating drug-protein interaction network with drug side effects. - Sayaka Mizutani; Edouard Pauwels; Véronique Stoven; Susumu Goto; Yoshihiro Yamanishi (Bioinform., 2012)

Identification of chemogenomic features from drug-target interaction networks using interpretable classifiers. - Yasuo Tabei; Edouard Pauwels; Véronique Stoven; Kazuhiro Takemoto; Yoshihiro Yamanishi (Bioinform., 2012)

Drug target prediction using adverse event report systems: a pharmacogenomic approach. - Masataka Takarabe; Masaaki Kotera; Yosuke Nishimura; Susumu Goto; Yoshihiro Yamanishi (Bioinform., 2012)

Drug Side-Effect Prediction Based on the Integration of Chemical and Biological Spaces. - Yoshihiro Yamanishi; Edouard Pauwels; Masaaki Kotera (J. Chem. Inf. Model., 2012)

GENIES: gene network inference engine based on supervised analysis. - Masaaki Kotera; Yoshihiro Yamanishi; Yuki Moriya; Minoru Kanehisa; Susumu Goto (Nucleic Acids Res., 2012)

Succinct interval-splitting tree for scalable similarity search of compound-protein pairs with property constraints. - Yasuo Tabei; Akihiro Kishimoto; Masaaki Kotera; Yoshihiro Yamanishi (KDD, 2013)

Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets. - Masaaki Kotera; Yasuo Tabei; Yoshihiro Yamanishi; Toshiaki Tokimatsu; Susumu Goto (Bioinform., 2013)

Inferring protein domains associated with drug side effects based on drug-target interaction network. - Hiroaki Iwata; Sayaka Mizutani; Yasuo Tabei; Masaaki Kotera; Susumu Goto; Yoshihiro Yamanishi (BMC Syst. Biol., 2013)

KCF-S: KEGG Chemical Function and Substructure for improved interpretability and prediction in chemical bioinformatics. - Masaaki Kotera; Yasuo Tabei; Yoshihiro Yamanishi; Yuki Moriya; Toshiaki Tokimatsu; Minoru Kanehisa; Susumu Goto (BMC Syst. Biol., 2013)

Scalable prediction of compound-protein interactions using minwise hashing. - Yasuo Tabei; Yoshihiro Yamanishi (BMC Syst. Biol., 2013)

KEGG OC: a large-scale automatic construction of taxonomy-based ortholog clusters. - Akihiro Nakaya; Toshiaki Katayama; Masumi Itoh; Kazushi Hiranuka; Shuichi Kawashima; Yuki Moriya; Shujiro Okuda; Michihiro Tanaka; Toshiaki Tokimatsu; Yoshihiro Yamanishi; Akiyasu C. Yoshizawa; Minoru Kanehisa; et al. (Nucleic Acids Res., 2013)

Metabolome-scale prediction of intermediate compounds in multistep metabolic pathways with a recursive supervised approach. - Masaaki Kotera; Yasuo Tabei; Yoshihiro Yamanishi; Ai Muto; Yuki Moriya; Toshiaki Tokimatsu; Susumu Goto (Bioinform., 2014)

DINIES: drug-target interaction network inference engine based on supervised analysis. - Yoshihiro Yamanishi; Masaaki Kotera; Yuki Moriya; Ryusuke Sawada; Minoru Kanehisa; Susumu Goto (Nucleic Acids Res., 2014)

Metabolome-scale de novo pathway reconstruction using regioisomer-sensitive graph alignments. - Yoshihiro Yamanishi; Yasuo Tabei; Masaaki Kotera (Bioinform., 2015)

Large-Scale Prediction of Beneficial Drug Combinations Using Drug Efficacy and Target Profiles. - Hiroaki Iwata; Ryusuke Sawada; Sayaka Mizutani; Masaaki Kotera; Yoshihiro Yamanishi (J. Chem. Inf. Model., 2015)

Systematic Drug Repositioning for a Wide Range of Diseases with Integrative Analyses of Phenotypic and Molecular Data. - Hiroaki Iwata; Ryusuke Sawada; Sayaka Mizutani; Yoshihiro Yamanishi (J. Chem. Inf. Model., 2015)

Target-Based Drug Repositioning Using Large-Scale Chemical-Protein Interactome Data. - Ryusuke Sawada; Hiroaki Iwata; Sayaka Mizutani; Yoshihiro Yamanishi (J. Chem. Inf. Model., 2015)

Mining Discriminative Patterns from Graph Data with Multiple Labels and Its Application to Quantitative Structure-Activity Relationship (QSAR) Models. - Zheng Shao; Yuya Hirayama; Yoshihiro Yamanishi; Hiroto Saigo (J. Chem. Inf. Model., 2015)

Scalable Partial Least Squares Regression on Grammar-Compressed Data Matrices. - Yasuo Tabei; Hiroto Saigo; Yoshihiro Yamanishi; Simon J. Puglisi (KDD, 2016)

Simultaneous prediction of enzyme orthologs from chemical transformation patterns for de novo metabolic pathway reconstruction. - Yasuo Tabei; Yoshihiro Yamanishi; Masaaki Kotera (Bioinform., 2016)

Scalable Alignment Kernels via Space-Efficient Feature Maps. - Yasuo Tabei; Yoshihiro Yamanishi; Rasmus Pagh (CoRR, 2018)

Dual Convolutional Neural Network for Graph of Graphs Link Prediction. - Shonosuke Harada; Hirotaka Akita; Masashi Tsubaki; Yukino Baba; Ichigaku Takigawa; Yoshihiro Yamanishi; Hisashi Kashima (CoRR, 2018)

Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm. - Michio Iwata; Longhao Yuan; Qibin Zhao; Yasuo Tabei; Francois Berenger; Ryusuke Sawada; Sayaka Akiyoshi; Momoko Hamano; Yoshihiro Yamanishi (Bioinform., 2019)

Network-based characterization of drug-protein interaction signatures with a space-efficient approach. - Yasuo Tabei; Masaaki Kotera; Ryusuke Sawada; Yoshihiro Yamanishi (BMC Syst. Biol., 2019)

Chemoinformatics and structural bioinformatics in OCaml. - Francois Berenger; Kam Y. J. Zhang; Yoshihiro Yamanishi (J. Cheminformatics, 2019)

A Distance-Based Boolean Applicability Domain for Classification of High Throughput Screening Data. - Francois Berenger; Yoshihiro Yamanishi (J. Chem. Inf. Model., 2019)

Guest Editorial for the 16th Asia Pacific Bioinformatics Conference. - Yoshihiro Yamanishi; Yasubumi Sakakibara; Yi-Ping Phoebe Chen (IEEE ACM Trans. Comput. Biol. Bioinform., 2019)

Network-based characterization of disease-disease relationships in terms of drugs and therapeutic targets. - Midori Iida; Michio Iwata; Yoshihiro Yamanishi (Bioinform., 2020)

Space-Efficient Feature Maps for String Alignment Kernels. - Yasuo Tabei; Yoshihiro Yamanishi; Rasmus Pagh (Data Sci. Eng., 2020)

Ranking Molecules with Vanishing Kernels and a Single Parameter: Active Applicability Domain Included. - Francois Berenger; Yoshihiro Yamanishi (J. Chem. Inf. Model., 2020)

Dual graph convolutional neural network for predicting chemical networks. - Shonosuke Harada; Hirotaka Akita; Masashi Tsubaki; Yukino Baba; Ichigaku Takigawa; Yoshihiro Yamanishi; Hisashi Kashima (BMC Bioinform., 2020)

Lean-Docking: Exploiting Ligands' Predicted Docking Scores to Accelerate Molecular Docking. - Francois Berenger; Ashutosh Kumar 001; Kam Y. J. Zhang; Yoshihiro Yamanishi (J. Chem. Inf. Model., 2021)

TRIOMPHE: Transcriptome-Based Inference and Generation of Molecules with Desired Phenotypes by Machine Learning. - Kazuma Kaitoh; Yoshihiro Yamanishi (J. Chem. Inf. Model., 2021)

Transformer-based Objective-reinforced Generative Adversarial Network to Generate Desired Molecules. - Chen Li 027; Chikashige Yamanaka; Kazuma Kaitoh; Yoshihiro Yamanishi (IJCAI, 2022)

TRANSDIRE: data-driven direct reprogramming by a pioneer factor-guided trans-omics approach. - Ryohei Eguchi; Momoko Hamano; Michio Iwata; Toru Nakamura; Shinya Oki; Yoshihiro Yamanishi (Bioinform., 2022)

Small compound-based direct cell conversion with combinatorial optimization of pathway regulations. - Toru Nakamura; Michio Iwata; Momoko Hamano; Ryohei Eguchi; Jun-ichi Takeshita; Yoshihiro Yamanishi (Bioinform., 2022)

From drug repositioning to target repositioning: prediction of therapeutic targets using genetically perturbed transcriptomic signatures. - Satoko Namba; Michio Iwata; Yoshihiro Yamanishi (Bioinform., 2022)

Epigenetic landscape of drug responses revealed through large-scale ChIP-seq data analyses. - Zhaonan Zou; Michio Iwata; Yoshihiro Yamanishi; Shinya Oki (BMC Bioinform., 2022)

Scaffold-Retained Structure Generator to Exhaustively Create Molecules in an Arbitrary Chemical Space. - Kazuma Kaitoh; Yoshihiro Yamanishi (J. Chem. Inf. Model., 2022)

Pathway trajectory analysis with tensor imputation reveals drug-induced single-cell transcriptomic landscape. - Michio Iwata; Hiroaki Mutsumine; Yusuke Nakayama; Naomasa Suita; Yoshihiro Yamanishi (Nat. Comput. Sci., 2022)

MacGAN: A Moment-Actor-Critic Reinforcement Learning-Based Generative Adversarial Network for Molecular Generation. - Huidong Tang; Chen Li 027; Shuai Jiang; Huachong Yu; Sayaka Kamei; Yoshihiro Yamanishi; Yasuhiko Morimoto (APWeb-WAIM (1), 2023)

Mode Collapse Alleviation of Reinforcement Learning-based GANs in Drug Design. - Zongli Jiang; Zhenbo Wang; Jinli Zhang; Man Wu; Chen Li 027; Yoshihiro Yamanishi (BIBM, 2023)

SpotGAN: A Reverse-Transformer GAN Generates Scaffold-Constrained Molecules with Property Optimization. - Chen Li 027; Yoshihiro Yamanishi (ECML/PKDD (1), 2023)

EarlGAN: An enhanced actor-critic reinforcement learning agent-driven GAN for de novo drug design. - Huidong Tang; Chen Li 027; Shuai Jiang; Huachong Yu; Sayaka Kamei; Yoshihiro Yamanishi; Yasuhiko Morimoto (Pattern Recognit. Lett., 2023)

GxVAEs: Two Joint VAEs Generate Hit Molecules from Gene Expression Profiles. - Chen Li 027; Yoshihiro Yamanishi (AAAI, 2024)

TenGAN: Pure Transformer Encoders Make an Efficient Discrete GAN for De Novo Molecular Generation. - Chen Li 027; Yoshihiro Yamanishi (AISTATS, 2024)

Network-based identification of diagnosis-specific trans-omic biomarkers via integration of multiple omics data. - Md. Mamunur Rashid; Momoko Hamano; Midori Iida; Michio Iwata; Toshiyuki Ko; Seitaro Nomura; Issei Komuro; Yoshihiro Yamanishi (Biosyst., 2024)

Molecular Generative Adversarial Network with Multi-Property Optimization. - Huidong Tang; Chen Li 027; Sayaka Kamei; Yoshihiro Yamanishi; Yasuhiko Morimoto (CoRR, 2024)

Gx2Mol: De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning. - Chen Li; Yuki Matsukiyo; Yoshihiro Yamanishi (CoRR, 2024)

Revealing Comprehensive Food Functionalities and Mechanisms of Action through Machine Learning. - Nanako Inoue; Tomokazu Shibata; Yusuke Tanaka; Hiromu Taguchi; Ryusuke Sawada; Kenshin Goto; Shogo Momokita; Morihiro Aoyagi; Takashi Hirao; Yoshihiro Yamanishi (J. Chem. Inf. Model., 2024)

Transcriptionally Conditional Recurrent Neural Network for De Novo Drug Design. - Yuki Matsukiyo; Atsushi Tengeiji; Chen Li 027; Yoshihiro Yamanishi (J. Chem. Inf. Model., 2024)

De Novo Generation of Chemical Structures of Inhibitor and Activator Candidates for Therapeutic Target Proteins by a Transformer-Based Variational Autoencoder and Bayesian Optimization. - Yuki Matsukiyo; Chikashige Yamanaka; Yoshihiro Yamanishi (J. Chem. Inf. Model., 2024)

Quantitative evaluation of molecular generation performance of graph-based GANs. - Jinli Zhang; Zhenbo Wang; Zongli Jiang; Man Wu; Chen Li 027; Yoshihiro Yamanishi (Softw. Qual. J., 2024)

AI-driven transcriptome profile-guided hit molecule generation. - Chen Li 027; Yoshihiro Yamanishi (Artif. Intell., 2025)

A Reinforcement Learning-Driven Transformer GAN for Molecular Generation. - Chen Li 027; Huidong Tang; Ye Zhu 002; Yoshihiro Yamanishi (CoRR, 2025)