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ORCIDYoshihiro Yamanishi
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2020 – today
- 2024
[j51]Md. Mamunur Rashid
, Momoko Hamano, Midori Iida, Michio Iwata, Toshiyuki Ko, Seitaro Nomura, Issei Komuro, Yoshihiro Yamanishi:
Network-based identification of diagnosis-specific trans-omic biomarkers via integration of multiple omics data. Biosyst. 236: 105122 (2024)- [j50]Yuki Matsukiyo, Chikashige Yamanaka, Yoshihiro Yamanishi:
De Novo Generation of Chemical Structures of Inhibitor and Activator Candidates for Therapeutic Target Proteins by a Transformer-Based Variational Autoencoder and Bayesian Optimization. J. Chem. Inf. Model. 64(7): 2345-2355 (2024) - [j49]Nanako Inoue, Tomokazu Shibata, Yusuke Tanaka, Hiromu Taguchi, Ryusuke Sawada, Kenshin Goto, Shogo Momokita, Morihiro Aoyagi, Takashi Hirao, Yoshihiro Yamanishi:
Revealing Comprehensive Food Functionalities and Mechanisms of Action through Machine Learning. J. Chem. Inf. Model. 64(14): 5712-5724 (2024) - [j48]Yuki Matsukiyo, Atsushi Tengeiji, Chen Li, Yoshihiro Yamanishi:
Transcriptionally Conditional Recurrent Neural Network for De Novo Drug Design. J. Chem. Inf. Model. 64(15): 5844-5852 (2024) - [j47]Jinli Zhang, Zhenbo Wang, Zongli Jiang, Man Wu, Chen Li, Yoshihiro Yamanishi:
Quantitative evaluation of molecular generation performance of graph-based GANs. Softw. Qual. J. 32(2): 791-819 (2024) - [c18]Chen Li, Yoshihiro Yamanishi:
GxVAEs: Two Joint VAEs Generate Hit Molecules from Gene Expression Profiles. AAAI 2024: 13455-13463 - [c17]Chen Li, Yoshihiro Yamanishi:
TenGAN: Pure Transformer Encoders Make an Efficient Discrete GAN for De Novo Molecular Generation. AISTATS 2024: 361-369 - [i4]Huidong Tang, Chen Li, Sayaka Kamei, Yoshihiro Yamanishi, Yasuhiko Morimoto:
Molecular Generative Adversarial Network with Multi-Property Optimization. CoRR abs/2404.00081 (2024) - 2023
- [j46]Huidong Tang, Chen Li, Shuai Jiang, Huachong Yu, Sayaka Kamei, Yoshihiro Yamanishi, Yasuhiko Morimoto:
EarlGAN: An enhanced actor-critic reinforcement learning agent-driven GAN for de novo drug design. Pattern Recognit. Lett. 175: 45-51 (2023) - [c16]Huidong Tang, Chen Li, Shuai Jiang, Huachong Yu, Sayaka Kamei, Yoshihiro Yamanishi, Yasuhiko Morimoto:
MacGAN: A Moment-Actor-Critic Reinforcement Learning-Based Generative Adversarial Network for Molecular Generation. APWeb-WAIM (1) 2023: 127-141 - [c15]Zongli Jiang, Zhenbo Wang, Jinli Zhang, Man Wu, Chen Li, Yoshihiro Yamanishi:
Mode Collapse Alleviation of Reinforcement Learning-based GANs in Drug Design. BIBM 2023: 3045-3052 - [c14]Chen Li, Yoshihiro Yamanishi:
SpotGAN: A Reverse-Transformer GAN Generates Scaffold-Constrained Molecules with Property Optimization. ECML/PKDD (1) 2023: 323-338 - 2022
- [j45]Ryohei Eguchi, Momoko Hamano, Michio Iwata, Toru Nakamura, Shinya Oki, Yoshihiro Yamanishi:
TRANSDIRE: data-driven direct reprogramming by a pioneer factor-guided trans-omics approach. Bioinform. 38(10): 2839-2846 (2022) - [j44]Zhaonan Zou, Michio Iwata, Yoshihiro Yamanishi, Shinya Oki:
Epigenetic landscape of drug responses revealed through large-scale ChIP-seq data analyses. BMC Bioinform. 23(1): 51 (2022) - [j43]Kazuma Kaitoh, Yoshihiro Yamanishi:
Scaffold-Retained Structure Generator to Exhaustively Create Molecules in an Arbitrary Chemical Space. J. Chem. Inf. Model. 62(9): 2212-2225 (2022) - [j42]Michio Iwata, Hiroaki Mutsumine, Yusuke Nakayama, Naomasa Suita, Yoshihiro Yamanishi:
Pathway trajectory analysis with tensor imputation reveals drug-induced single-cell transcriptomic landscape. Nat. Comput. Sci. 2(11): 758-770 (2022) - [c13]Chen Li, Chikashige Yamanaka, Kazuma Kaitoh, Yoshihiro Yamanishi:
Transformer-based Objective-reinforced Generative Adversarial Network to Generate Desired Molecules. IJCAI 2022: 3884-3890 - 2021
- [j41]Francois Berenger, Ashutosh Kumar, Kam Y. J. Zhang, Yoshihiro Yamanishi:
Lean-Docking: Exploiting Ligands' Predicted Docking Scores to Accelerate Molecular Docking. J. Chem. Inf. Model. 61(5): 2341-2352 (2021) - [j40]Kazuma Kaitoh, Yoshihiro Yamanishi:
TRIOMPHE: Transcriptome-Based Inference and Generation of Molecules with Desired Phenotypes by Machine Learning. J. Chem. Inf. Model. 61(9): 4303-4320 (2021) - 2020
- [j39]Midori Iida, Michio Iwata, Yoshihiro Yamanishi:
Network-based characterization of disease-disease relationships in terms of drugs and therapeutic targets. Bioinform. 36(Supplement-1): i516-i524 (2020) - [j38]Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki, Yukino Baba, Ichigaku Takigawa, Yoshihiro Yamanishi, Hisashi Kashima:
Dual graph convolutional neural network for predicting chemical networks. BMC Bioinform. 21-S(3): 94 (2020) - [j37]Yasuo Tabei, Yoshihiro Yamanishi, Rasmus Pagh:
Space-Efficient Feature Maps for String Alignment Kernels. Data Sci. Eng. 5(2): 168-179 (2020) - [j36]Francois Berenger, Yoshihiro Yamanishi:
Ranking Molecules with Vanishing Kernels and a Single Parameter: Active Applicability Domain Included. J. Chem. Inf. Model. 60(9): 4376-4387 (2020)
2010 – 2019
- 2019
- [j35]Michio Iwata, Longhao Yuan, Qibin Zhao, Yasuo Tabei, Francois Berenger, Ryusuke Sawada, Sayaka Akiyoshi, Momoko Hamano, Yoshihiro Yamanishi:
Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm. Bioinform. 35(14): i191-i199 (2019) - [j34]Yasuo Tabei, Masaaki Kotera, Ryusuke Sawada, Yoshihiro Yamanishi:
Network-based characterization of drug-protein interaction signatures with a space-efficient approach. BMC Syst. Biol. 13-S(2): 39:1-39:15 (2019) - [j33]Francois Berenger, Kam Y. J. Zhang, Yoshihiro Yamanishi:
Chemoinformatics and structural bioinformatics in OCaml. J. Cheminformatics 11(1): 10:1-10:13 (2019) - [j32]Francois Berenger, Yoshihiro Yamanishi:
A Distance-Based Boolean Applicability Domain for Classification of High Throughput Screening Data. J. Chem. Inf. Model. 59(1): 463-476 (2019) - [j31]Yoshihiro Yamanishi, Yasubumi Sakakibara, Yi-Ping Phoebe Chen:
Guest Editorial for the 16th Asia Pacific Bioinformatics Conference. IEEE ACM Trans. Comput. Biol. Bioinform. 16(1): 1-2 (2019) - [c12]Yasuo Tabei, Yoshihiro Yamanishi, Rasmus Pagh:
Space-Efficient Feature Maps for String Alignment Kernels. ICDM 2019: 1312-1317 - 2018
- [i3]Yasuo Tabei, Yoshihiro Yamanishi, Rasmus Pagh:
Scalable Alignment Kernels via Space-Efficient Feature Maps. CoRR abs/1802.06382 (2018) - [i2]Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki, Yukino Baba, Ichigaku Takigawa, Yoshihiro Yamanishi, Hisashi Kashima:
Dual Convolutional Neural Network for Graph of Graphs Link Prediction. CoRR abs/1810.02080 (2018) - 2016
- [j30]Yasuo Tabei, Yoshihiro Yamanishi, Masaaki Kotera:
Simultaneous prediction of enzyme orthologs from chemical transformation patterns for de novo metabolic pathway reconstruction. Bioinform. 32(12): 278-287 (2016) - [c11]Yasuo Tabei, Hiroto Saigo, Yoshihiro Yamanishi, Simon J. Puglisi:
Scalable Partial Least Squares Regression on Grammar-Compressed Data Matrices. KDD 2016: 1875-1884 - [i1]Yasuo Tabei, Hiroto Saigo, Yoshihiro Yamanishi, Simon J. Puglisi:
Scalable Partial Least Squares Regression on Grammar-Compressed Data Matrices. CoRR abs/1606.05031 (2016) - 2015
- [j29]Yoshihiro Yamanishi, Yasuo Tabei, Masaaki Kotera:
Metabolome-scale de novo pathway reconstruction using regioisomer-sensitive graph alignments. Bioinform. 31(12): 161-170 (2015) - [j28]Hiroaki Iwata, Ryusuke Sawada, Sayaka Mizutani, Yoshihiro Yamanishi:
Systematic Drug Repositioning for a Wide Range of Diseases with Integrative Analyses of Phenotypic and Molecular Data. J. Chem. Inf. Model. 55(2): 446-459 (2015) - [j27]Zheng Shao, Yuya Hirayama, Yoshihiro Yamanishi, Hiroto Saigo:
Mining Discriminative Patterns from Graph Data with Multiple Labels and Its Application to Quantitative Structure-Activity Relationship (QSAR) Models. J. Chem. Inf. Model. 55(12): 2519-2527 (2015) - [j26]Hiroaki Iwata, Ryusuke Sawada, Sayaka Mizutani, Masaaki Kotera, Yoshihiro Yamanishi:
Large-Scale Prediction of Beneficial Drug Combinations Using Drug Efficacy and Target Profiles. J. Chem. Inf. Model. 55(12): 2705-2716 (2015) - [j25]Ryusuke Sawada, Hiroaki Iwata, Sayaka Mizutani, Yoshihiro Yamanishi:
Target-Based Drug Repositioning Using Large-Scale Chemical-Protein Interactome Data. J. Chem. Inf. Model. 55(12): 2717-2730 (2015) - 2014
- [j24]Masaaki Kotera, Yasuo Tabei, Yoshihiro Yamanishi, Ai Muto, Yuki Moriya, Toshiaki Tokimatsu, Susumu Goto:
Metabolome-scale prediction of intermediate compounds in multistep metabolic pathways with a recursive supervised approach. Bioinform. 30(12): 165-174 (2014) - [j23]Yoshihiro Yamanishi, Masaaki Kotera, Yuki Moriya, Ryusuke Sawada, Minoru Kanehisa, Susumu Goto:
DINIES: drug-target interaction network inference engine based on supervised analysis. Nucleic Acids Res. 42(Webserver-Issue): 39-45 (2014) - 2013
- [j22]Masaaki Kotera, Yasuo Tabei, Yoshihiro Yamanishi, Toshiaki Tokimatsu, Susumu Goto:
Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets. Bioinform. 29(13): 135-144 (2013) - [j21]Hiroaki Iwata, Sayaka Mizutani, Yasuo Tabei, Masaaki Kotera, Susumu Goto, Yoshihiro Yamanishi:
Inferring protein domains associated with drug side effects based on drug-target interaction network. BMC Syst. Biol. 7(S-6): S18 (2013) - [j20]Masaaki Kotera, Yasuo Tabei, Yoshihiro Yamanishi, Yuki Moriya, Toshiaki Tokimatsu, Minoru Kanehisa, Susumu Goto:
KCF-S: KEGG Chemical Function and Substructure for improved interpretability and prediction in chemical bioinformatics. BMC Syst. Biol. 7(S-6): S2 (2013) - [j19]Yasuo Tabei, Yoshihiro Yamanishi:
Scalable prediction of compound-protein interactions using minwise hashing. BMC Syst. Biol. 7(S-6): S3 (2013) - [j18]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, Susumu Goto:
KEGG OC: a large-scale automatic construction of taxonomy-based ortholog clusters. Nucleic Acids Res. 41(Database-Issue): 353-357 (2013) - [c10]Yasuo Tabei, Akihiro Kishimoto, Masaaki Kotera, Yoshihiro Yamanishi:
Succinct interval-splitting tree for scalable similarity search of compound-protein pairs with property constraints. KDD 2013: 176-184 - 2012
- [j17]Yasuo Tabei, Edouard Pauwels, Véronique Stoven, Kazuhiro Takemoto, Yoshihiro Yamanishi:
Identification of chemogenomic features from drug-target interaction networks using interpretable classifiers. Bioinform. 28(18): 487-494 (2012) - [j16]Sayaka Mizutani, Edouard Pauwels, Véronique Stoven, Susumu Goto, Yoshihiro Yamanishi:
Relating drug-protein interaction network with drug side effects. Bioinform. 28(18): 522-528 (2012) - [j15]Masataka Takarabe, Masaaki Kotera, Yosuke Nishimura, Susumu Goto, Yoshihiro Yamanishi:
Drug target prediction using adverse event report systems: a pharmacogenomic approach. Bioinform. 28(18): 611-618 (2012) - [j14]Yoshihiro Yamanishi, Edouard Pauwels, Masaaki Kotera:
Drug Side-Effect Prediction Based on the Integration of Chemical and Biological Spaces. J. Chem. Inf. Model. 52(12): 3284-3292 (2012) - [j13]Masaaki Kotera, Yoshihiro Yamanishi, Yuki Moriya, Minoru Kanehisa, Susumu Goto:
GENIES: gene network inference engine based on supervised analysis. Nucleic Acids Res. 40(Web-Server-Issue): 162-167 (2012) - 2011
- [j12]Edouard Pauwels, Véronique Stoven, Yoshihiro Yamanishi:
Predicting drug side-effect profiles: a chemical fragment-based approach. BMC Bioinform. 12: 169 (2011) - [j11]Yoshihiro Yamanishi, Edouard Pauwels, Hiroto Saigo, Véronique Stoven:
Extracting Sets of Chemical Substructures and Protein Domains Governing Drug-Target Interactions. J. Chem. Inf. Model. 51(5): 1183-1194 (2011) - 2010
- [j10]Yoshihiro Yamanishi, Masaaki Kotera, Minoru Kanehisa, Susumu Goto:
Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinform. 26(12): 246-254 (2010) - [j9]Hisashi Kashima, Satoshi Oyama, Yoshihiro Yamanishi, Koji Tsuda:
Cartesian Kernel: An Efficient Alternative to the Pairwise Kernel. IEICE Trans. Inf. Syst. 93-D(10): 2672-2679 (2010)
2000 – 2009
- 2009
- [j8]Yoshihiro Yamanishi, Masahiro Hattori, Masaaki Kotera, Susumu Goto, Minoru Kanehisa:
E-zyme: predicting potential EC numbers from the chemical transformation pattern of substrate-product pairs. Bioinform. 25(12) (2009) - [j7]Kevin Bleakley, Yoshihiro Yamanishi:
Supervised prediction of drug-target interactions using bipartite local models. Bioinform. 25(18): 2397-2403 (2009) - [j6]Hisashi Kashima, Yoshihiro Yamanishi, Tsuyoshi Kato, Masashi Sugiyama, Koji Tsuda:
Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: a semi-supervised approach. Bioinform. 25(22): 2962-2968 (2009) - [c9]Hisashi Kashima, Satoshi Oyama, Yoshihiro Yamanishi, Koji Tsuda:
On Pairwise Kernels: An Efficient Alternative and Generalization Analysis. PAKDD 2009: 1030-1037 - [c8]Hisashi Kashima, Tsuyoshi Kato, Yoshihiro Yamanishi, Masashi Sugiyama, Koji Tsuda:
Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction. SDM 2009: 1100-1111 - 2008
- [j5]Minoru Kanehisa, Michihiro Araki, Susumu Goto, Masahiro Hattori, Mika Hirakawa, Masumi Itoh, Toshiaki Katayama, Shuichi Kawashima, Shujiro Okuda, Toshiaki Tokimatsu, Yoshihiro Yamanishi:
KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36(Database-Issue): 480-484 (2008) - [c7]Yoshihiro Yamanishi, Michihiro Araki, Alex Gutteridge, Wataru Honda, Minoru Kanehisa:
Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. ISMB 2008: 232-240 - [c6]Yoshihiro Yamanishi:
Supervised Bipartite Graph Inference. NIPS 2008: 1841-1848 - 2007
- [j4]Yoshihiro Yamanishi, Francis R. Bach, Jean-Philippe Vert:
Glycan classification with tree kernels. Bioinform. 23(10): 1211-1216 (2007) - [c5]Tetsuya Sato, Yoshihiro Yamanishi, Katsuhisa Horimoto, Minoru Kanehisa, Hiroyuki Toh:
Inference of Protein-Protein Interactions by Using Co-evolutionary Information. AB 2007: 322-333 - 2006
- [j3]Tetsuya Sato, Yoshihiro Yamanishi, Katsuhisa Horimoto, Minoru Kanehisa, Hiroyuki Toh:
Partial correlation coefficient between distance matrices as a new indicator of protein-protein interactions. Bioinform. 22(20): 2488-2492 (2006) - 2005
- [j2]Tetsuya Sato, Yoshihiro Yamanishi, Minoru Kanehisa, Hiroyuki Toh:
The inference of protein-protein interactions by co-evolutionary analysis is improved by excluding the information about the phylogenetic relationships. Bioinform. 21(17): 3482-3489 (2005) - [j1]Yoshihiro Yamanishi, Yutaka Tanaka:
Sensitivity analysis in functional principal component analysis. Comput. Stat. 20(2): 311-326 (2005) - [c4]Yoshihiro Yamanishi, Jean-Philippe Vert, Minoru Kanehisa:
Supervised enzyme network inference from the integration of genomic data and chemical information. ISMB (Supplement of Bioinformatics) 2005: 468-477 - 2004
- [c3]Yoshihiro Yamanishi, Jean-Philippe Vert, Minoru Kanehisa:
Protein network inference from multiple genomic data: a supervised approach. ISMB/ECCB (Supplement of Bioinformatics) 2004: 363-370 - [c2]Jean-Philippe Vert, Yoshihiro Yamanishi:
Supervised Graph Inference. NIPS 2004: 1433-1440 - 2003
- [c1]Yoshihiro Yamanishi, Jean-Philippe Vert, Akihiro Nakaya, Minoru Kanehisa:
Extraction of correlated gene clusters from multiple genomic data by generalized kernel canonical correlation analysis. ISMB (Supplement of Bioinformatics) 2003: 323-330
Coauthor Index
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