Publications

Papers

Peer-reviewed papers

Miura, S., Sawada, R., Yorita, A., Kida, H., Kamada, T., and Yamanishi, Y.,: "A trial of topiramate for patients with hereditary spinocerebellar ataxia",
Clinical Case Reports, 11, e6980, 2023.
[pdf] [journal site] [pubmed]
DOI: 10.1002/ccr3.6980
Iwata, M., Mutsumine, H., Nakayama, Y., Suita, N., and Yamanishi, Y.,: "Pathway trajectory analysis with tensor imputation reveals drug-induced single-cell transcriptomic landscape",
Nature Computational Science, 2, 758–770, 2022.
[pdf] [journal site] [NATURE research briefing] [KYUTECH press release] [pubmed]
DOI: 10.1038/s43588-022-00352-8
Iwata, M., Kosai, K., Ono, Y., Oki, S., Mimori, K., and Yamanishi, Y.,: "Regulome-based characterization of drug activity across the human diseasome",
npj Systems Biology and Applications, 8:44, 2022.
[pdf] [journal site] [pubmed]
DOI: 10.1038/s41540-022-00255-4
Niimura, T., Zamami, Y., Miyata, K., Mikami, T., Asada, M., Fukushima, K., Yoshino, M., Mitsuboshi, S., Okada, N., Hamano, H., Sakurada, T., Matsuoka-Ando, R., Aizawa, F., Yagi, K., Goda, M., Chuma, M., Koyama, T., Izawa-Ishizawa, Y., Yanagawa, H., Fujino, H., Yamanishi, Y., and Ishizawa, K.,: "Characterization of Immune Checkpoint Inhibitor–Induced Myasthenia Gravis Using the US Food and Drug Administration Adverse Event Reporting System",
The Journal of Clinical Pharmacology, 2022.
[pdf] [journal site] [pubmed]
DOI: 10.1002/jcph.2187
Ando-Matsuoka, R., Yagi, K., Takaoka, M., Sakajiri, Y., Shibata, T., Sawada, R., Maruo, A., Miyata, K., Aizawa, F., Hamano, H., Niimura, T., Izawa-Ishizawa, Y., Goda, M., Sakaguchi, S., Zamami, Y., Yamanishi, Y., and Ishizawa, K.,: "Differential effects of proton pump inhibitors and vonoprazan on vascular endothelial growth factor expression in cancer cells",
Drug Development Research, 2022.
[pdf] [journal site] [pubmed]
DOI: 10.1002/ddr.22013
Nakamura, T., Iwata, M., Hamano, M., Eguchi, R., Takeshita, J., and Yamanishi, Y.,: "Small compound-based direct cell conversion with combinatorial optimization of pathway regulations",
Bioinformatics, 38:ii99-ii105, 2022. (Special Issue of ECCB2022: 25 accepted papers out of 144 submissions = 14 %)
[pdf] [journal website] [pubmed] [KYUTECH press release]
DOI: 10.1093/bioinformatics/btac475
Li, C., Yamanaka, C., Kaitoh, K. and Yamanishi, Y.,: "Transformer-Based Objective-Reinforced Generative Adversarial Network to Generate Desired Molecules",
Proceedings of the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI 2022), 3884-3890, 2022. (681 accepted papers out of 4535 submissions = 15 %)
[pdf] [conference video] [conference site] [pubmed]
DOI: 10.24963/ijcai.2022/539
Namba, S., Iwata, M., and Yamanishi, Y.,: "From drug repositioning to target repositioning: prediction of therapeutic targets using genetically perturbed transcriptomic signatures",
Bioinformatics, 38:i68-i76, 2022. (Special Issue of ISMB2022: 48 accepted papers out of 242 submissions = 19 %)
[pdf] [journal website] [pubmed] [KYUTECH press release]
DOI: 10.1093/bioinformatics/btac240
Eguchi, R., Hamano, M., Iwata, M., Nakamura, T., Oki, S., and Yamanishi, Y.,: "TRANSDIRE: data-driven direct reprogramming by a pioneer factor-guided trans-omics approach",
Bioinformatics, 38(10), 2839-2846, 2022.
[pdf] [journal website] [pubmed] [KYUTECH press release]
DOI: 10.1093/bioinformatics/btac209
Nemoto, W., Yamanishi, Y., Limviphuvadh, V., Fujishiro, S., Shimamura, S., Fukushima, A., and Toh, H.,: "A web server for GPCR-GPCR interaction pair prediction",
Frontiers in Endocrinology, Volume 13, Article 825195, 2022.
[pdf] [pubmed]
DOI: 10.3389/fendo.2022.825195
Kaitoh, K. and Yamanishi, Y.,: "Scaffold-Retained Structure Generator to Exhaustively Create Molecules in an Arbitrary Chemical Space",
Journal of Chemical Information and Modeling, 62(9), 2212–2225, 2022.
[pdf] [journal site] [pubmed]
DOI: 10.1021/acs.jcim.1c01130
Zou, Z., Iwata, M. Yamanishi, Y., and Oki, S.,: "Epigenetic landscape of drug responses revealed through large-scale ChIP-seq data analyses",
BMC Bioinformatics, 23(1):51, 2022.
[pdf] [journal site] [pubmed]
DOI: 10.1186/s12859-022-04571-8
Kaitoh, K. and Yamanishi, Y.,: "TRIOMPHE: Transcriptome-based Inference and Generation of Molecules with Desired Phenotypes by Machine Learning",
Journal of Chemical Information and Modeling, 61(9), 4303–4320, 2021.
[pdf] [journal site] [pubmed]
DOI: 10.1021/acs.jcim.1c00967
Miura, S. Kamada, T., Fujioka, R. and Yamanishi, Y.,: "Plasma amino acids in patients with essential tremor",
Clinical Case Reports, 9(8):e04580, 2021.
[pdf] [journal site] [pubmed]
DOI: 10.1002/ccr3.4580
Berenger, F. Kumar, A., Zhang, K. and Yamanishi, Y.,: "Lean-Docking: Exploiting Ligands’ Predicted Docking Scores to Accelerate Molecular Docking",
Journal of Chemical Information and Modeling, 24;61(5):2341-2352, 2021.
[pdf] [journal site] [pubmed]
DOI: 10.1021/acs.jcim.0c01452
Hamano, M., Nomura, S., Iida, M., Komuro, I., and Yamanishi, Y.,: "Prediction of single-cell mechanisms for disease progression in hypertrophic remodelling by a trans-omics approach",
Scientific Reports, 11(1):8112, 2021
[pdf] [journal website] [pubmed]
DOI: 10.1038/s41598-021-86821-y
Fujii, A., Masuda, T., Iwata, M., Tobo, T., Wakiyama, H., Koike, K., Kosai, K., Nakano, T., Kuramitsu, S., Kitagawa, A., Sato, K., Kouyama, Y., Shimizu, D., Matsumoto, Y., Utsunomiya, T., Ohtsuka, T., Yamanishi, Y., Nakamura, M., and Mimori, K.,: "The novel driver gene ASAP2 is a potential druggable target in pancreatic cancer",
Cancer Science, 112(4):1655-1668, 2021
[pdf] [journal website] [pubmed]
DOI: 10.1111/cas.14858
Iida, M., Iwata, M., and Yamanishi, Y.,: "Network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets",
Bioinformatics, 36, i516–i524, 2020. (Special Issue of ISMB2020: 64 accepted papers out of 329 submissions = 19 %)
[pdf] [journal website] [pubmed] [JST press release] [KYUTECH press release]
DOI: 10.1093/bioinformatics/btaa439
Berenger, F. and Yamanishi, Y.,: "Ranking Molecules with Vanishing Kernels and a Single Parameter: Active Applicability Domain Included",
Journal of Chemical Information and Modeling, 60, 9, 4376–4387, 2020.
[pdf] [journal site] [pubmed]
DOI: 10.1021/acs.jcim.9b01075
Tabei, Y., Yamanishi, Y., and Pagh, R.,: "Space-efficient Feature Maps for String Alignment Kernels",
Data Science and Engineering, 5, 168–179, 2020.
[pdf] [journal site]
DOI: 10.1007/s41019-020-00120-6
Amano, Y., Honda, H., Sawada, R., Nukada, Y., Yamane, M., Ikeda, N., Morita, O., and Yamanishi, Y.,: "In silico systems for predicting chemical-induced side effects using known and potential chemical protein interactions, enabling mechanism estimation",
Journal of Toxicological Sciences, 45(3):137-149, 2020.
[pdf] [journal site] [pubmed]
DOI: 10.2131/jts.45.137
Fukunaga, I., Sawada, R., Shibata, T., Kaitoh, K., Sakai, Y., and Yamanishi, Y.,: "Prediction of the Health Effects of Food Peptides and Elucidation of the Mode-of-action Using Multi-task Graph Convolutional Neural Network",
Molecular Informatics, 39(1-2):e1900134, 2020.
[pdf] [journal site] [pubmed]
DOI: 10.1002/minf.201900134
Akiyoshi, S., Iwata, M., Berenger, F., and Yamanishi, Y.,: "Omics-based identification of glycan structures as biomarkers for a variety of diseases",
Molecular Informatics, 39(1-2):e1900112, 2020.
[pdf] [journal site] [pubmed]
DOI: 10.1002/minf.201900112
Harada, S., Akita, H., Tsubaki, M., Baba, Y., Takigawa, I., Yamanishi, Y., and Kashima, H.,: "Dual Graph Convolutional Neural Network for Predicting Chemical Networks",
BMC Bioinformatics, 21, Article number: 94, 2020.
[pdf] [journal site] [pubmed]
DOI: 10.1186/s12859-020-3378-0
Tabei, Y., Yamanishi, Y., and Pagh, R.,: "Space-efficient Feature Maps for Scalable Alignment Kernels",
Proceedings of the 19th IEEE International Conference on Data Mining (ICDM2019), 1312-1317, 2019.
[pdf][journal site]
DOI:
Iwata, M., Yuan, L., Zhao, Q., Tabei, Y., Berenger, F., Sawada, R., Akiyoshi, S., Hamano, M., and Yamanishi, Y.,: "Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm",
Bioinformatics, 35 i191–i199, 2019. (Special Issue of ISMB/ECCB2019: 69 accepted papers out of 366 submissions = 18 %)
[pdf] [journal website] [pubmed] [JST press release] [RIKEN press release] [KYUTECH press release]
DOI: 10.1093/bioinformatics/btz313
Berenger, F., Zhang, K., and Yamanishi, Y.,: "Chemoinformatics and Structural Bioinformatics in OCaml",
Journal of Cheminformatics, 11(1),10, 2019.
[pdf] [journal site] [pubmed]
DOI: 10.1186/s13321-019-0332-0
Berenger, F. and Yamanishi, Y.,: "A Distance-Based Boolean Applicability Domain for Classification of High Throughput Screening Data",
Journal of Chemical Information and Modeling, 59(1), 463-476, 2019.
[pdf][journal site][pubmed]
DOI: 10.1021/acs.jcim.8b00499
Kamada, T., Miura, S., Kida, H., Irie, K., Yamanishi, Y., Hoshino, T., and Taniwaki, T.,: "MIBG Myocardial Scintigraphy in Progressive Supranuclear Palsy",
Journal of the Neurological Sciences, 396, 3-7, 2019.
[pdf] [journal site][pubmed]
DOI: 10.1016/j.jns.2018.10.019
Tabei, Y., Kotera, M., Sawada, R., and Yamanishi, Y.,: "Network-based characterization of drug-protein interaction signatures with a space-efficient approach",
BMC Systems Biology, 13(Suppl 2):39, 2019. (Special Issue of APBC2019)
[pdf] [journal site] [pubmed]
DOI: 10.1186/s12918-019-0691-1
Iwata, M., Hirose, L., Kohara, H., Liao, J., Sawada, R., Akiyoshi, S., Tani, K., and Yamanishi, Y.,: "Pathway-based drug repositioning for cancers: computational prediction and experimental validation",
Journal of Medicinal Chemistry, 61(21), 9583−9595, 2018.
[pdf] [journal site][pubmed] [press release]
DOI: 10.1021/acs.jmedchem.8b01044
Sawada, R., Iwata, M., Umezaki, M., Usui, Y., Kobayashi, T., Kubono, T., Hayashi, S., Kadowaki, M., and Yamanishi, Y.,: "KampoDB, database of predicted targets and functional annotations of natural medicines",
Scientific Reports, 8:11216, 2018.
[pdf] [journal site] [pubmed] [press release]
DOI: 10.1038/s41598-018-29516-1
Kida, H., Miura, S., Yamanishi, Y., Takahashi, T., Kamada, T., Yorita, A., Ayabe, M., Kida, H., Hoshino, T., and Taniwaki, T.,: "Cervical dystonia in Parkinson's disease: Retrospective study of later-stage clinical features",
Neurology Asia, 23(3), 245-251, 2018.
[pdf] [journal site] [pubmed]
DOI:
Sawada, R., Iwata, M., Tabei, Y., Yamato, H., and Yamanishi, Y.,: "Predicting inhibitory and activatory drug targets by chemically and genetically perturbed transcriptome signatures",
Scientific Reports, 8:156, 2018.
[pdf] [journal site] [pubmed]
DOI: 10.1038/s41598-017-18315-9
Iwata, M., Sawada, R., Iwata, H., Kotera, M., and Yamanishi, Y.,: "Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics",
Scientific Reports, 7:40164, 2017.
[pdf] [journal site] [pubmed]
DOI: 10.1038/srep40164
Tabei, Y., Saigo, H., Yamanishi, Y., and Puglisi, S.J.,: "Scalable partial least squares regression on grammar-compressed data matrices",
Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2016), 1875-1884, ACM New York, NY, USA, 2016. (142 accepted papers out of 784 submissions = 18 %)
[pdf]
DOI: 10.1145/2939672.2939864
Nemoto, W., Yamanishi, Y., Limviphuvadh, V., Saito, A., and Toh, H.,: "GGIP: structure and sequence-based GPCR-GPCR interaction pair predictor",
PROTEINS: Structure, Function, and Bioinformatics, 84(9), 1224-1233, 2016.
[pdf] [pubmed]
DOI: 10.1002/prot.25071
Tabei, Y.*, Yamanishi, Y.*, and Kotera, M. (* Joint first author),: "Simultaneous prediction of enzyme orthologs from chemical transformation patterns for de novo metabolic pathway reconstruction",
Bioinformatics, 32(12), i278-i287, 2016. (Special Issue of ISMB2016: 41 accepted papers out of 187 submissions = 21 %)
[pdf] [pubmed]
DOI: 10.1093/bioinformatics/btw260
Hizukuri, Y., Sawada, R., and Yamanishi, Y.,: "Predicting target proteins for drug candidate compounds based on drug-induced gene expression data in a chemical structure-independent manner",
BMC Medical Genomics, 8:82 (10 pages), 2015.
[pdf] [pubmed] [pubmed]
DOI: 10.1186/s12920-015-0158-1
Sawada, R., Iwata, H., Mizutani, S., and Yamanishi, Y.,: "Target-based drug repositioning using large-scale chemical-protein interactome data",
Journal of Chemical Information and Modeling, 55(12), 2717–2730, 2015.
[pdf] [pubmed]
DOI: 10.1021/acs.jcim.5b00330
Shao, Z. Hirayama, Y., Yamanishi, Y., and Saigo, H.,: "Mining discriminative patterns from graph data with multiple labels and its application to QSAR",
Journal of Chemical Information and Modeling, 55(12), 2519–2527, 2015.
[pdf] [pubmed]
DOI: 10.1021/acs.jcim.5b00376
Yamanishi, Y.*, Tabei, Y.*, and Kotera, M. (* Joint first author),: "Metabolome-scale de novo pathway reconstruction using regioisomer-sensitive graph alignments",
Bioinformatics, 31(12), i161-i170, 2015. (Special Issue of ISMB/ECCB2015: 40 accepted papers out of 241 submissions = 20 %; 27 papers accepted at the first round review = 11 %)
[pdf] [pubmed]
DOI: 10.1093/bioinformatics/btv224
Iwata, H., Sawada, R., Mizutani, S., Kotera, M., and Yamanishi, Y.,: "Large-scale prediction of beneficial drug combinations using drug efficacy and target profiles",
Journal of Chemical Information and Modeling, 55(12), 2705–2716, 2015.
[pdf] [pubmed]
DOI: 10.1021/acs.jcim.5b00444
Iwata, H., Sawada, R., Mizutani, S., and Yamanishi, Y.,: "Systematic drug repositioning for a wide range of diseases with integrative analyses of phenotypic and molecular data",
Journal of Chemical Information and Modeling, 55(2), 446–459, 2015.
[pdf] [pubmed]
DOI: 10.1021/ci500670q
Sawada, R., Kotera, M., and Yamanishi, Y.,: "Benchmarking a wide range of chemical descriptors for drug-target interaction prediction using a chemogenomic approach",
Molecular Informatics, 33, 719-731, 2014. (Invited review paper)
[pdf] [pubmed]
DOI: 10.1002/minf.201400066
Yamanishi, Y.*, Kotera, M.*, Moriya, Y.*, Sawada, R., Kanehisa, M., and Goto, S. (* Joint first author),: "DINIES: drug-target interaction network inference engine based on supervised analysis",
Nucleic Acids Research, 42, W39-W45, 2014
[pdf] [server] [pubmed]
DOI: 10.1093/nar/gku337
Kotera, M.*, Tabei, Y.*, Yamanishi, Y.*, Muto, A., Moriya, Y., Tokimatsu, T., and Goto, S. (* Joint first author),: "Metabolome-scale prediction of intermediate compounds in multi-step metabolic pathways with a recursive supervised approach",
Bioinformatics, 30, i165-i174, 2014. (Special Issue of ISMB2014: 37 accepted papers out of 191 submissions = 19 %)
[pdf] [pubmed]
DOI: 10.1093/bioinformatics/btu265
Yamanishi, Y.,: "Inferring chemogenomic features from drug-target interaction networks",
Molecular Informatics, 32(11-12), 991–999, 2013. (Special Issue: Chemogenomics) (Invited review paper)
[pdf] [pubmed]
DOI: 10.1002/minf.201300079
Iwata, H., Mizutani, S., Tabei, Y., Kotera, M., Goto, S., and Yamanishi, Y.,: "Inferring protein domains associated with drug side effects based on drug-target interaction network",
BMC Systems Biology, 7(Suppl 6):S18, 2013 (Special Issue of GIW2013)
[pdf] [pubmed]
DOI: 10.1186/1752-0509-7-S6-S18
Tabei, Y. and Yamanishi, Y.,: "Scalable prediction of compound-protein interactions using minwise hashing",
BMC Systems Biology, 7(Suppl 6):S3, 2013 (Special Issue of GIW2013)
[pdf] [pubmed]
DOI: 10.1186/1752-0509-7-S6-S3
Kotera, M.*, Tabei, Y.*, Yamanishi, Y.*, Moriya, Y., Tokimatsu, T., Kanehisa, M., and Goto, S. (* Joint first author),: "KCF-S: KEGG Chemical Function and Substructure for improved interpretability and prediction in chemical bioinformatics",
BMC Systems Biology, 7(Suppl 6):S2, 2013 (Special Issue of GIW2013)
DOI: 10.1186/1752-0509-7-S6-S2
Tabei, Y., Kishimoto, A., Kotera, M., and Yamanishi, Y.,: "Succinct Interval Splitting Tree for Scalable Similarity Search of Compound-Protein Pairs with Property Constraints",
Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2013), 176-184, ACM New York, NY, USA, 2013.(126 accepted papers out of 726 submissions = 17 %)
[pdf] [supplements]
DOI: 10.1145/2487575.2487637
Kotera, M.*, Tabei, Y.*, Yamanishi, Y.*, Tokimatsu, T., and Goto, S. (* Joint first author),: "Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets",
Bioinformatics, 29(13), i135-i144, 2013.(Special Issue of ISMB/ECCB2013: 40 accepted papers out of 247 submissions = 16 %)
[pdf] [pubmed]
DOI: 10.1093/bioinformatics/btt244
Nakaya, A., Katayama, T., Itoh, M., Hiranuka, K., Kawashima, S., Moriya, Y., Okuda, S., Tanaka, M., Tokimatsu, T., Yamanishi, Y., Yoshizawa, A., Kanehisa, M., and Goto, S.,: "KEGG OC: A large-scale automatic construction of taxonomy-based ortholog clusters",
Nucleic Acids Research, 41, D353-D357, 2013.
[pdf] [database] [pubmed]
DOI: 10.1093/nar/gks1239
Yamanishi, Y., Pauwels, E., and Kotera, M.,: "Drug side-effect prediction based on the integration of chemical and biological spaces",
Journal of Chemical Information and Modeling, 52, No.12, 3284-3292, 2012.
[pdf] [supplements] [pubmed]
DOI: 10.1021/ci2005548
Nakajima, N., Tamura, T., Yamanishi, Y., Horimoto, K. and Akutsu, T.,: "Network completion using dynamic programming and least-squares fitting",
The Scientific World Journal, 957620 (8 pages), 2012.
[pdf] [pubmed]
DOI: 10.1100/2012/957620
Takarabe, M., Kotera, M., Nishimura, Y., Goto, S., and Yamanishi, Y.,: "Drug target prediction using adverse event report systems: a pharmacogenomic approach",
Bioinformatics, 28, i611-i618, 2012. (Special Issue of ECCB2012: 48 accepted papers out of 341 submissions = 14 %)
[pdf] [supplements] [pubmed]
DOI: 10.1093/bioinformatics/bts413
Mizutani, S., Pauwels, E., Stoven, V., Goto, S., and Yamanishi, Y.,: "Relating drug-protein interaction network with drug side-effects",
Bioinformatics, 28, i522-i528, 2012. (Special Issue of ECCB2012: 48 accepted papers out of 341 submissions = 14 %)
[pdf] [supplements] [pubmed]
DOI:　10.1093/bioinformatics/bts383
Tabei, Y., Pauwels, E., Stoven, V., Takemoto, K., and Yamanishi, Y.,: "Identification of chemogenomic features from drug-target interaction networks using interpretable classifiers",
Bioinformatics, 28, i487-i494, 2012. (Special Issue of ECCB2012: 48 accepted papers out of 341 submissions = 14 %)
[pdf] [supplements] [pubmed]
DOI: 10.1093/bioinformatics/bts412
Kotera, M.*, Yamanishi, Y.*, Moriya, Y.*, Kanehisa, M., and Goto, S. (* Joint first author),: "GENIES: gene network inference engine based on supervised analysis",
Nucleic Acids Research, 40, W162-W167, 2012.
[pdf] [server] [pubmed]
DOI: 10.1093/nar/gks459
Pauwels, E., Stoven, V., and Yamanishi, Y.,: "Predicting drug side-effect profiles: a chemical fragment-based approach",
BMC Bioinformatics, 12:169, 2011.
[pdf] [supplements] [pubmed]
DOI: 10.1186/1471-2105-12-169
Yamanishi, Y., Pauwels, E., Saigo, H. and Stoven, V.,: "Extracting sets of chemical substructures and protein domains governing drug-target interactions",
Journal of Chemical Information and Modeling, 51(5), 1183-1194, 2011.
[pdf] [supplements] [pubmed]
DOI: 10.1021/ci100476q
Kashima, H., Oyama, S., Yamanishi, Y., and Tsuda, K.: "Cartesian Kernel: An Efficient Alternative to the Pairwise Kernel",
IEICE Transaction on Information and Systems, E93-D, No. 10, 2672-2679, 2010.
DOI:
Tamura, T., Yamanishi, Y., Tanabe, M., Goto, S., Kanehisa, M., Horimoto, K., and Akutsu, T.,: "Integer programming-based method for completing signaling pathways and its application to analysis of colorectal cancer",
Genome Informatics, 24, 193-203, 2010. (Special Issue of IBSB2010)
[pubmed]
DOI:
Yamanishi, Y., Kotera, M., Kanehisa, M., and Goto, S.,: "Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework",
Bioinformatics, 26, i246-i254, 2010. (Special Issue of ISMB2010: 46 accepted papers out of 240 submissions = 19 %)
[pdf] [supplements] [pubmed]
DOI: 10.1093/bioinformatics/btq176
Kashima, H., Yamanishi, Y., Kato, T., Sugiyama, M., and Tsuda, K.,: "Simultaneous Inference of Biological Networks of Multiple Species from Genome-wide Data and Evolutionary Information: A Semi-supervised Approach",
Bioinformatics, 25, 2962-2968, 2009.
[pdf] [supplements] [pubmed]
DOI: 10.1093/bioinformatics/btp494
Bleakley, K. and Yamanishi, Y.,: "Supervised prediction of drug-target interactions using bipartite local models",
Bioinformatics, 25, 2397-2403, 2009.
[pdf] [supplements] [pubmed]
DOI: 10.1093/bioinformatics/btp433
Yamanishi, Y.*, Hattori, M.*, Kotera, M.*, Goto, S., and Kanehisa, M. (* Joint first author),: "E-zyme: predicting potential EC numbers from the chemical transformation pattern of substrate-product pairs",
Bioinformatics, 25, i179-i186, 2009. (Special Issue of ISMB/ECCB2009: 46 accepted papers out of 242 submissions = 18 %)
[pdf] [server] [pubmed]
DOI: 10.1093/bioinformatics/btp223
Kashima, H., Oyama, S., Yamanishi, Y., and Tsuda, K.: "On Pairwise Kernels: An Efficient Alternative and Generalization Analysis",
Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Lecture Notes in Artificial Intelligence, 5476, 1030-1037, 2009.
DOI: 10.1007/978-3-642-01307-2_110
Kashima, H., Kato, T., Yamanishi, Y., Sugiyama, M., and Tsuda, K.: "Link Propagation: A Fast-semi Supervised Learning Algorithm for Link Prediction",
Proceedings of the 9th SIAM Conference on Data Mining (SDM), 1099-1110, 2009.
DOI: 10.1137/1.9781611972795.94
Yamanishi, Y.,: "Supervised Bipartite Graph Inference",
Advances in Neural Information Processing Systems 21 (Koller, D., Schuurmans, D., Bengio, Y. and Bottou, L. eds.), 1841-1848, MIT Press, Cambridge, MA, 2009. (Proceedings of NIPS2008: 123 oral presentations and 250 accepted papers out of 1022 submissions = 12 %)
[pdf]
DOI:
Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., and Kanehisa, M.,: "Prediction of drug-target interaction networks from the integration of chemical and genomic spaces",
Bioinformatics, 24, i232-i240, 2008. (Special Issue of ISMB2008: 49 accepted papers out of 287 submissions = 17 %)
[pdf] [supplements] [pubmed]
DOI: 10.1093/bioinformatics/btn162
Kanehisa, M., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., Katayama, T., Kawashima, S., Okuda, S., Tokimatsu, T., and Yamanishi, Y.,: "KEGG for linking genomes to life and the environment",
Nucleic Acids Res., 36, D480-D484, 2008.
[pdf] [pubmed]
DOI: 10.1093/nar/gkm882
Suga, A., Yamanishi, Y., Hashimoto, K., Goto, S., and Kanehisa, M.,: "An improved scoring scheme for predicting glycan structures from gene expression data",
Genome Informatics, 18(1), 237-246, 2007. (Special Issue of IBSB2007)
[pdf] [pubmed]
DOI:
Sato, T., Yamanishi, Y., Horimoto, K., Kanehisa, M., and Toh, H.,: "Inference of Protein-Protein Interactions by Using Co-evolutionary Information",
Proceedings of the 2nd international conference on Algebraic Biology (Anai, H., Horimoto, K. and Kutsia, T. eds.), Lecture Notes in Computer Science 4545, p.322-333, Springer, Heidelberg, 2007.
DOI:
Yamanishi, Y. and Vert, J.-P.,: "Kernel matrix regression",
Proceedings of the 12th International Conference on Applied Stochastic Models and Data Analysis (ASMDA 2007), 2007.
The longer version is found in Technical Report HAL-00133355, 2007.
[html]
DOI:
Yamanishi, Y., Bach, F., and Vert, J.-P.,: "Glycan Classification with Tree Kernels",
Bioinformatics, 23(10), 1211-1216, 2007.
[pdf] [supplements] [pubmed]
DOI: 10.1093/bioinformatics/btm090
Yamanishi, Y., Mihara, H., Osaki, M., Muramatsu, H., Esaki, N., Sato, T., Hizukuri, Y., Goto, S., and Kanehisa, M.,: "Prediction of missing enzyme genes in a bacterial metabolic network: Reconstruction of the lysine-degradation pathway of Pseudomonas aeruginosa",
FEBS Journal, 274, 2262-2273, 2007.
[pdf] [pubmed]
DOI: 10.1111/j.1742-4658.2007.05763.x
Okamoto, S., Yamanishi, Y., Ehira, S., Kawashima, S., Tonomura, K., and Kanehisa, M.,: "Prediction of nitrogen metabolism-related genes in Anabaena by kernel-based network analysis",
Proteomics, 7(6), 900-909, 2007.
[pdf] [pubmed]
DOI: 10.1002/pmic.200600862
Sato, T., Yamanishi, Y., Horimoto, K., Kanehisa, M., and Toh, H.,: "Partial correlation coefficient between distance matrices as a new indicator of protein-protein interactions",
Bioinformatics, 22(20), 2488-2492, 2006.
[pdf] [supplements] [pubmed]
DOI: 10.1093/bioinformatics/btl419
Yamanishi, Y. and Tanaka, Y.,: "Sensitivity Analysis in Kernel Principal Component Analysis",
Compstat 2006: Proceedings in Computational Statistics, Rizzi, A. and Vichi, M. (Eds.), 787-794, Physica-Verlag/Springer, 2006.
[pdf]
DOI:
Yamanishi, Y. and Vert, J.-P.,: "Estimating Protein Network from Multiple Genomic Data by Kernel Methods",
Proceedings of the Institute of Statistical Mathematics, 54-2, 357-373, 2006. (in Japanese)
[pdf]
DOI:
Hizukuri, Y., Yamanishi, Y., Nakamura, O., Yagi, F., Goto, S., and Kanehisa, M.,: "Extraction of leukemia specific glycan motifs by computational comparative glycomics",
Carbohydrate Research, 340-14, 2270-2278, 2005.
[pdf] [pubmed]
DOI: 10.1016/j.carres.2005.07.012
Sato, T., Yamanishi, Y., Kanehisa, M., and Toh, H.,: "The inference of protein-protein interactions by co-evolutionary analysis is improved by excluding phylogenetic relationships",
Bioinformatics, 21-17, 3482-3489, 2005.
[pdf] [supplements] [pubmed]
DOI: 10.1093/bioinformatics/bti564
Yamanishi, Y., Vert, J.-P. and Kanehisa, M.,: "Supervised Enzyme Network Inference from the Integration of Genomic Data and Chemical Information",
Bioinformatics, 21, i468-i477, 2005. (Special Issue of ISMB2005: 56 accepted papers out of 428 submissions = 13 %)
[pdf] [supplements] [pubmed]
DOI: 10.1093/bioinformatics/bti1012
Tamori, A., Yamanishi, Y., Kawashima, S., Kanehisa, M., Enomoto, M., Tanaka, H., Kubo, S., Shiomi, S., and Nishiguchi, S.,: "Alteration of Gene Expression in Hepatitis B Virus DNA integrated human hepatocellular carcinoma",
Clinical Cancer Research, 11-16, 5821-5826, 2005.
[pubmed]
DOI: 10.1158/1078-0432.CCR-04-2055
Vert, J.-P. and Yamanishi, Y.,: "Supervised graph inference",
Advances in Neural Information Processing Systems 17, Lawrence K. Saul and Yair Weiss and Leon Bottou (Eds.), 1433-1440, 2005. (Proceedings of NIPS2004: Oral presentation, 24 accepted papers as oral out of 882 = 3%)
[pdf]
DOI:
Yamanishi, Y. and Tanaka, Y.,: "Sensitivity Analysis in Functional Principal Component Analysis",
Computational Statistics, 20-2, 313-329, 2005.
[pdf]
DOI: 10.1007/BF02789706
Yamanishi, Y., Vert, J.-P. and Kanehisa, M.,: "Protein Network Inference from Multiple Genomic Data: A Supervised Approach",
Bioinformatics, 20, i363-i370, 2004. (Special Issue of ISMB2004: 50 accepted papers out of 492 submissions = 10 %)
[pdf] [supplements] [pubmed]
DOI: 10.1093/bioinformatics/bth910
Hizukuri, Y., Yamanishi, Y., Hashimoto, K. and Kanehisa, M.,: "Extraction of Species-specific Glycan Substructures",
Genome Informatics, 15, 69-81, 2004. (Special Issue of IBSB2004)
[pdf] [pubmed]
DOI:
Yamanishi, Y., Vert, J.-P., Nakaya, A. and Kanehisa, M.,: "Extraction of Correlated Gene Clusters from Multiple Genomic Data by Generalized Kernel Canonical Correlation Analysis",
Bioinformatics, 19, i323-i330, 2003. (Special Issue of ISMB2003: 35 accepted papers out of 242 submissions = 14 %)
[pdf] [pubmed]
DOI: 10.1093/bioinformatics/btg1045
Yamanishi, Y. and Tanaka, Y.,: "Geographically Weighted Functional Multiple Regression Analysis: A Numerical Investigation",
Journal of Japanese Society of Computational Statistics, 15(2), 307-317, 2003.
[pdf]
DOI:
Yamanishi, Y., Itoh, M. and Kanehisa, M.,: "Extraction of Organism Groups from Phylogenetic Profiles Using Independent Component Analysis",
Genome Informatics, 13, 61-70, 2002. (Special Issue of GIW2002)
[pdf] [pubmed]
DOI:

Books

Books and book chapters

Yamanishi, Y.,: Data-driven drug discovery and repositioning by machine learning methods",
Chemical Information Bulletin, A Publication of the Division of Chemical Information of the ACS (Currano, J.N., eds.), Winter 2018, 70(4), 48-52, 2018.
[link to publisher] [link to amazon]
Iwata, M. and Yamanishi, Y.,: The use of large-scale chemically-induced transcriptome data acquired from LINCS to study small molecules",
Systems Chemical Biology, Methods in Molecular Biology Series (Ziegler, S. and Waldmann, H., eds.), Springer, 189-203, 2018.
[link to publisher] [link to amazon]
Yamanishi, Y.,: Linear and Kernel Model Construction Methods for Predicting Drug–Target Interactions in a Chemogenomic Framework",
Computational Chemogenomics, Methods in Molecular Biology Series (Brown, J.B., eds.), Springer, 355-368, 2018.
[link to publisher] [link to amazon]
Yamanishi, Y.,: Sparse modeling to analyze drug-target interaction networks",
Data Mining for Systems Biology, Methods in Molecular Biology Series (Mamitsuka, H., eds.), Springer, 181-193, 2018.
[link to publisher] [link to amazon]
Yamanishi, Y., Tabei, Y.,and Kotera, M.,: Statistical machine learning for agriculture and human healthcare based on biomedical big data",
Agriculture as a metaphor for creativity in all human endeavors (Proceedings of Forum "Math-for-Industry" 2016), 111-124, Springer, 2018.
[pdf]
Yamanishi, Y.,: Statistical Methods to Predict Drug Side-Effects",
Post-Genomic Approaches in Drug and Vaccine Development (Sakharkar, K.R., Sakharkar, M.K, and Chandra, R. eds.), River Publishers, 381-395, 2015.
[link to publisher] [link to amazon]
Yamanishi, Y.,: Chemogenomic approaches to infer drug-target interaction networks",
Data Mining for Systems Biology, Methods in Molecular Biology Series (DeLisi, C., Kanehisa, M. and Mamitsuka, H., eds.), Springer, 97-113, 2012.
[link to publisher] [link to amazon]
Lodhi, H. and Yamanishi, Y.,: Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques, IGI Global, 2010.
[link to publisher] [link to amazon]
Yamanishi, Y. and Kashima, H.: Prediction of compound-protein interactions with machine learning methods",
Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques (Lodhi, H. and Yamanishi, Y., eds.), 304-317, 2010.
[link to publisher] [link to amazon]
Sato T., Yamanishi, Y., Kanehisa M., Horimoto K. and Toh H.,: Improvement of the mirrortree method by extracting evolutionary information",
Sequence and Genome Analysis: Methods and Applications (Zhao, Z., eds.), 129-139, iConcept Press, 2010.
[link to publisher] [link to amazon]
Yamanishi, Y.,: Supervised inference of metabolic networks from the integration of genomic data and chemical information",
Elements of Computational Systems Biology (Lodhi, H. and S. Muggleton, S., eds.), 189-212, Wiley, 2010.
[link to publisher] [link to amazon]
Yamanishi, Y., Vert, J.-P., and Kanehisa, M.,: Heterogeneous data comparison and gene selection with kernel canonical correlation analysis",
Kernel Methods in Computational Biology (Schoelkopf, B. and Tsuda, K. and Vert, J.-P., eds.), 209-230, MIT Press, 2004.
[link to publisher] [pdf]

Conference

Talks at international conferences

Yamanishi, Y.,: "Data-driven drug discovery and molecular design by machine learning",
Inserm/JSPS joint seminar on artificial intelligence and big data approaches in precision medicine and health science (HP), Yamaguchi, Dec.3-Dec.4 (presentation on Dec. 4), 2022. [Invited talk]
Yamanishi, Y.,: "Data-driven drug discovery and molecular design by machine learning",
The 7th Autumn School of Chemoinformatics in Nara 2022 (HP), Nara, Nov.29-Nov.30 (presentation on Nov. 29), 2022. [Invited talk]
Nakamura, T., Iwata, M., Hamano, M., Eguchi, R., Takeshita, J., and Yamanishi, Y.,: "Small compound-based direct cell conversion with combinatorial optimization of pathway regulations",
The 21st European Conference on Computational Biology (ECCB2022), Barcelona, Spain, Sep.18-Sep.21, 2022
Yamanishi, Y.,: "Data-driven drug discovery and healthcare by machine learning",
The Eighteenth International Conference on Intelligent Computing (ICIC2022) (HP), Xi'an, China, Aug.7-Aug.11 (presentation date: Aug. 9), 2022. [Invited talk]
Li, C., Yamanaka, C., Kaitoh, K. and Yamanishi, Y.,: "Transformer-Based Objective-Reinforced Generative Adversarial Network to Generate Desired Molecules",
The 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI 2022) (IJCAI-ECAI 2022), Vienna, Austria, Jul.23-Jul.29, 2022
Namba, S., Iwata, M., and Yamanishi, Y.,: "Network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets",
The 30th International Conference on Intelligent Systems for Molecular Biology (ISMB2022), Madison, USA, Jul.10-Jul.14, 2022
Yamanishi, Y.,: "Large-scale analysis of drug-induced transcriptome data by machine learning",
The 2nd ASHBi SignAC Workshop Integrating Single-cell Analysis and Mathematics (HP), online, Dec.10-Dec.10, 2021. [Invited talk]
Iida, M., Iwata, M., and Yamanishi, Y.,: "Network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets",
The 28th International Conference on Intelligent Systems for Molecular Biology (ISMB2020), Online, Jul.13-Jul.16, 2020
Kaitoh, K. and Yamanishi, Y.,: "Rational Drug Molecular Design Using Deep Neural Network",
The 8-th French-Japanese Workshop on Computational Methods in Chemistry (FJCMC2020) (HP), Kumamoto, Mar.19-Nov.20, 2020. [Invited talk]
Berenger, F. and Yamanishi, Y.,: "A Distance-Based Boolean Applicability Domain for Classification of High Throughput Screening Data",
The 8-th French-Japanese Workshop on Computational Methods in Chemistry (FJCMC2020) (HP), Kumamoto, Mar.19-Nov.20, 2020. [Invited talk]
Yamanishi, Y.,: "Data-driven drug discovery and healthcare by machine learning",
The 8-th French-Japanese Workshop on Computational Methods in Chemistry (FJCMC2020) (HP), Kumamoto, Mar.19-Nov.20, 2020. [Invited talk]
Yamanishi, Y.,: "Data-driven drug discovery and medical treatment by machine learning",
The 6th Autumn School of Chemoinformatics in Nara 2019 (HP), Nara, Nov.27-Nov.28, 2019. [Invited talk]
Yamanishi, Y.,: "Network-based and data-driven drug discovery by machine learning",
BioNetVisA workshop at the 20th International Conference on Systems Biology (ICSB2019), Okinawa, Japan, Oct.31, 2019. [Invited talk]
Yamanishi, Y.,: "Data-driven drug discovery and medical treatment by machine learning",
ACS Fall 2019 National Meeting & Exposition, Herman Skolnik Symposium, San Diego, USA, Aug.27-Aug.27, 2019. [Invited talk]
Iwata, M., Yuan, L., Zhao, Q., Tabei, Y., Berenger, F., Sawada, R., Akiyoshi, S., Hamano, M., and Yamanishi, Y.,: "Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm",
The 27th International Conference on Intelligent Systems for Molecular Biology & 18th European Conference on Computational Biology (ISMB/ECCB2019), Basel, Switzerland, Jul.21-Jul.25, 2019
Tabei, Y., Kotera, M., Sawada, R., and Yamanishi, Y.,: "Network-based characterization of drug-protein interaction signatures with a space-efficient approach",
The Seventeenth Asia Pacific Bioinformatics Conference (APBC2019), Wuhan, China, Jan.14-Jan.16, 2019.
Yamanishi, Y.,: "Data-driven drug discovery and repositioning by machine learning methods",
ACS Fall 2018 National Meeting & Exposition, Herman Skolnik Symposium, "De novo design - Automating drug discovery" session(HP), Boston, USA, Aug.21-Aug.21, 2018. [Invited talk]
Berenger, F. and Yamanishi, Y.,: "Combining a bisector tree with the Tanimoto distance for similarity searches and beyond",
The 7th French-Japanese Workshop on Computational Methods in Chemistry (HP), Strasbourg, France, Jul.1-Jul.2, 2018.
Yamanishi, Y.,: "Data-driven drug discovery and repositioning by machine learning",
The 5th Autumn School of Chemoinformatics in Nara 2017 (HP), Nara, Nov.15-Nov.16, 2017. [Invited talk]
Yamanishi, Y.,: "Statistical machine learning approaches for agriculture and human healthcare based on biomedical big data",
Forum "Math-for-Industry" 2016 (FMfI2016), Brisbane, Australia, Nov.21-Nov.23, 2016. [Invited talk]
Yamanishi, Y.,: "Statistical machine learning for drug discovery",
First Kyushu-UNSW Joint Workshop on the Mathematics underpinning Industry and Innovation, Sydney, Australia, Nov.18, 2016. [Invited talk]
Sawada, R., Iwata, M., and Yamanishi, Y.,: "Transomics-based drug repositioning for a wide range of diseases",
The 26th Hot Spring Harbor International Symposium, Fukuoka, Japan, Nov.2-3, 2016.
Tabei, Y., Yamanishi, Y., and Kotera, M.,: "Simultaneous prediction of enzyme orthologs from chemical transformation patterns for de novo metabolic pathway reconstruction",
The 24th International Conference on Intelligent Systems for Molecular Biology (ISMB2016), Orlando, Florida, USA, Jul.8-Jul.12, 2016.
Yamanishi, Y.,: "Computational drug repositioning using medical big data",
The 6th French-Japanese Workshop on Computational Methods in Chemistry (HP), Kyoto, Mar.16-Mar.17, 2016.
Yamanishi, Y.,: "Systematic drug repositioning via omics data analysis with machine learning methods",
Autumn School of Chemoinformatics in Tokyo (HP), Tokyo, Nov.25-Nov.26, 2015. [Invited talk]
Yamanishi, Y.,: "Systematic drug repositioning for a wide range of diseases with machine learning methods",
Study Sessions on Bioinformatics and Related Topics, Osaka, Nov.4, 2015. [Invited talk]
Yamanishi, Y., Tabei, Y., and Kotera, M.,: "Metabolome-scale de novo pathway reconstruction using regioisomer-sensitive graph alignments",
The 23rd International Conference on Intelligent Systems for Molecular Biology & 14th European Conference on Computational Biology (ISMB/ECCB2015), Dublin, Ireland, Jul.10-Jul.14, 2015.
Yamanishi, Y.,: "Systematic drug repositioning for a wide range of diseases with computational approaches",
JCUP VI (JCUP VI), Tokyo, Jun.4-Jun.5, 2015. [Invited talk]
Yamanishi, Y.,: "Analysis and inference of drug-target interaction networks",
International Symposium on Bioinformatics and its Application (ISBA), Tokyo, Japan, Sep.30, 2014. [Invited talk]
Kotera, M., Tabei, Y., Yamanishi, Y., Muto, A., Moriya, Y., Tokimatsu, T., and Goto, S.,: "Metabolome-scale prediction of intermediate compounds in multi-step metabolic pathways with a recursive supervised approach",
The 22nd International Conference on Intelligent Systems for Molecular Biology (ISMB2014), Boston, USA, Jul.11-Jul.15, 2014.
Yamanishi, Y.,: "Analysis and inference of drug-target interaction networks",
The 5th French-Japanese Workshop on Computational Methods in Chemistry (HP), Strasbourg, France, Jun.30-Jul.1, 2014.
Yamanishi, Y.,: "In silico methods for predicting drug targets and new drug indications",
The 9th International Symposium of the Institute Network, Osaka, Japan, Jun.19-Jun.20, 2014.
Yamanishi, Y.,: "Analysis and inference of drug-target interaction networks",
The 2nd BMIRC International Symposium on Advances in Bioinformatics and Medical Engineering (BMIRC2014), Iizuka, Japan, Jan.29-Jan.30, 2014. [Invited talk]
Yamanishi, Y.,: "Predicting drug-target interaction networks from the integration of chemical, genomic, and pharmacological spaces",
International Symposium on Tumor Biology in Kanazawa & Academic Drug Discovery Symposium (HP), Kanazawa, Japan, Jan.23-Jan.24, 2014. [Invited talk]
Iwata, H., Mizutani, S., Tabei, Y., Kotera, M., Goto, S., and Yamanishi, Y.,: "Inferring protein domains associated with drug side effects based on drug-target interaction network",
The 24th International Conference on Genome Informatics (GIW2013), Singapore, Singapore, Dec.16-18, 2013.
Tabei, Y. and Yamanishi, Y.,: "Scalable prediction of compound-protein interactions using minwise hashing",
The 24th International Conference on Genome Informatics (GIW2013), Singapore, Singapore, Dec.16-18, 2013.
Kotera, M., Tabei, Y., Yamanishi, Y., Moriya, Y., Tokimatsu, T., Kanehisa, M., and Goto, S.,: "KCF-S: KEGG Chemical Function and Substructure for improved interpretability and prediction in chemical bioinformatics",
The 24th International Conference on Genome Informatics (GIW2013), Singapore, Singapore, Dec.16-18, 2013.
Tabei, Y., Kishimoto, A., Kotera, M., and Yamanishi, Y.,: "Succinct Interval Splitting Tree for Scalable Similarity Search of Compound-Protein Pairs with Property Constraints",
The 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2013), Chicago, USA, Aug.11-14, 2013.
Kotera, M., Tabei, Y., Yamanishi, Y., Tokimatsu, T., and Goto, S.,: "Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets",
The 21st International Conference on Intelligent Systems for Molecular Biology & 12th European Conference on Computational Biology (ISMB/ECCB2013), Berlin, Germany, Jul.19-Jul.23, 2013.
Takarabe, M., Kotera, M., Nishimura, Y., Goto, S., and Yamanishi, Y.,: "Drug target prediction using adverse event report systems: a pharmacogenomic approach",
The 11th European Conference on Computational Biology (ECCB2012), Basel, Switzerland, Sep.9-Sep.12, 2012.
Mizutani, S., Pauwels, E., Stoven, V., Goto, S., and Yamanishi, Y.,: "Relating drug-protein interaction network with drug side-effects",
The 11th European Conference on Computational Biology (ECCB2012), Basel, Switzerland, Sep.9-Sep.12, 2012.
Tabei, Y., Pauwels, E., Stoven, V., Takemoto, K., and Yamanishi, Y.,: "Identification of chemogenomic features from drug-target interaction networks using interpretable classifiers",
The 11th European Conference on Computational Biology (ECCB2012), Basel, Switzerland, Sep.9-Sep.12, 2012.
Yamanishi, Y.,: "Machine learning methods to analyze and infer drug-target interaction networks",
2012 Sapporo Workshop on Machine Learning and Applications to Biology (MLAB2012), Sapporo, Japan, Aug.6-Aug.7, 2012. [Invited talk]
Yamanishi, Y.,: "Predicting drug-target interaction networks from the integration of chemical, genomic, and pharmacological spaces",
The 2012 workshop on statistical methods for post-genomic data (SMPGD2012), Lyon, France, Jan.26-Jan.27, 2012. [Invited talk]
Yamanishi, Y., Pauwels, E., Saigo, H. and Stoven, V.,: "Identification of chemogenomic features from drug-target interaction networks by sparse canonical correspondence analysis",
The 5th International Workshop on Machine Learning in Systems Biology (MLSB2011): A satellite of the 19th International Conference on Intelligent Systems for Molecular Biology (ISMB2011), Wienna, Austria, Jul.20-Jul.21, 2011.
Tamura, T., Yamanishi, Y., Tanabe, M., Goto, S., Kanehisa, M., Horimoto, K., and Akutsu, T.,: "Integer programming-based method for completing signaling pathways and its application to analysis of colorectal cancer",
The 10th International Workshop on Bioinformatics & Systems Biology (IBSB2010), Uji, Japan, Jul.25-Jul.28, 2010.
Yamanishi, Y., Kotera, M., Kanehisa, M., and Goto, S.,: "Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework",
The 18th International Conference on Intelligent Systems for Molecular Biology (ISMB2010), Bonston, USA, Jul.8-Jul.13, 2010.
Yamanishi, Y.,: "Prediction of drug-target interactions from the integration of chemical, genomic and pharmacological data",
Workshop on Bioinformatics for Medical and Pharmaceutical Research (website), Paris, France, Nov.16-Nov.17, 2009. [Invited talk]
Yamanishi, Y., Hattori, M., Kotera, M., Goto, S., and Kanehisa, M.,: "E-zyme: predicting potential EC numbers from the chemical transformation pattern of substrate-product pairs",
The 17th International Conference on Intelligent Systems for Molecular Biology & 8th European Conference on Computational Biology (ISMB/ECCB2009), Stockholm, Sweden, Jun.27-Jul.2, 2009.
Yamanishi, Y.,: "Supervised bipartite graph inference",
The 22nd Annual Conference on Neural Information Processing Systems (NIPS2008), Vancouver and Whistler, British Columbia, Canada, Dec.8-Dec.11, 2008.
Yamanishi, Y.,: "Supervised bipartite graph inference: applications to predicting drug-target interactions",
The workshop on Statistic Mathematic and Applications, Frejus (La Villa Clythia), France, Sep.01-05, 2008. [Invited talk]
Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., and Kanehisa, M.,: "Prediction of drug-target interaction networks from the integration of chemical and genomic spaces",
The 16th International Conference on Intelligent Systems for Molecular Biology (ISMB2008), Toronto, Canada, Jun.20-Jun.23, 2008.
Suga, A., Yamanishi, Y., Hashimoto, K., Goto, S., and Kanehisa, M.,: "An improved scoring scheme for predicting glycan structures from gene expression data",
The 7th International Workshop on Bioinformatics & Systems Biology (IBSB2007), Tokyo, Japan, Jul.31-Aug.2, 2007.
Yamanishi, Y. and Vert, J.-P.,: "Kernel matrix regression",
The 12th International Conference on Applied Stochastic Models and Data Analysis (ASMDA2007), the special session "Supervised Prediction with Neural Networks and SVMs", Chania, Crete, Greece, May.29-Jun.1, 2007.
Tanaka, Y. and Yamanishi, Y.,: "On Some Measures of Influence in Sensitivity Analysis in Kernel Principal Component Analysis",
The 8th Workshop of the ERCIM Working Group on Matrix Computations and Statistics (website) (a sattelite of (COMPSTAT2006), Salerno, Italy, Sep.2-Sep.3, 2006.
Yamanishi, Y. and Tanaka, Y.,: "Sensitivity Analysis in Kernel Principal Component Analysis",
The 17th International Conference on Computational Statistics (COMPSTAT2006), the 17th Conference of IASC-ERS COMPSTAT2006), Rome, Italy, Aug.28-Sep.1, 2006.
Yamanishi, Y.,: "Metabolic Network Inference from Multiple Types of Genomic Data",
INRA Workshop on System Biology (INRA), Paris, France, Feb.2, 2006. [Invited talk]
Yamanishi, Y., Vert, J.-P. and Kanehisa, M.,: "Supervised Enzyme Network Inference from the Integration of Genomic Data and Chemical Information",
The 13th International Conference on Intelligent Systems for Molecular Biology (ISMB2005), Detroit, USA, Jun.26-Jun.29, 2005.
Vert, J.-P. and Yamanishi, Y.,: "Supervised graph inference",
The 18th Annual Conference on Neural Information Processing Systems (NIPS2004), 1433-1440, Vancouver and Whistler, British Columbia, Canada, Dec.14-Dec.16, 2004.
Yamanishi, Y., Vert, J.-P. and Kanehisa, M.,: "Protein Network Inference from Multiple Genomic Data: A Supervised Approach",
The 12th International Conference on Intelligent Systems for Molecular Biology (ISMB2004), Glasgow, Scotland, Jul.31-Aug.4, 2004.
Hizukuri, Y., Yamanishi, Y., Hashimoto, K. and Kanehisa, M.,: "Extraction of Species-specific Glycan Substructures",
The 4th International Workshop on Bioinformatics & Systems Biology (IBSB2004), Kyoto, Japan, May 31-Jun.3, 2004.
Yamanishi, Y., Vert, J.-P. and Kanehisa, M.,: "Detecting Correlations between Multiple Types of Genomic Data",
The 3rd International Workshop on Bioinformatics & Systems Biology (IBSB2003), Dresden, Germany, Aug.16-19, 2003.
Yamanishi, Y., Vert, J.-P., Nakaya, A. and Kanehisa, M.,: "Extraction of Correlated Gene Clusters from Multiple Genomic Data by Generalized Kernel Canonical Correlation Analysis",
The 11th International Conference on Intelligent Systems for Molecular Biology (ISMB2003), Brisbane, Australia, Jun.29-Jul.3, 2003.
Yamanishi, Y., Vert, J.-P. and Kanehisa, M.,: "Generalized Kernel Canonical Correlation Analysis for Multiple Genomic Data",
Workshop on Kernel Methods in Computational Biology (a satellite of (RECOMB2003), Berlin, Germany, Apr.14, 2003.
Yamanishi, Y., Itoh, M. and Kanehisa, M.,: "Extraction of Organism Groups from Phylogenetic Profiles Using Independent Component Analysis",
The 13th International Conference on Genome Informatics (GIW2002), 61-70, Tokyo, Japan, Dec.16-18, 2002.
Oide, N., Yamanishi, Y. and Tanaka, Y.,: "Sensitivity Analysis in Functional Principal Component Analysis",
The 4th Conference of Asian Regional Section of the International Association for Statistical Computing, Proceedings 205-208, Pusan, Korea, Dec.5-7, 2002. [ps] [pdf]
Yamanishi, Y.,: "Modeling spatial non-stationarity in functional regression",
Research Conference on High-dimensional Nonlinear Statistical Modeling, Proceedings 4, Wulkow, Germany, Sep.15-19, 2001.
Yamanishi, Y. and Tanaka, Y.,: "Geographically Weighted Functional Multiple Regression Analysis: A Numerical Investigation",
International Conference on New Trends in Computational Statistics with Biomedical Applications (ICNCB), Proceedings 287-294, Osaka, Japan, Aug.30-Sep.1, 2001. [ps] [pdf]
Yamanishi, Y. and Tanaka, Y.,: "Geographically Weighted Functional Regression Analysis",
The 53rd Session of the International Statistical Institute (ISI2001), Contributed Papers, 3, 149-150, Seoul, Korea, Aug.22-Aug.29, 2001. [ps] [pdf]
Yamanishi, Y. and Tanaka, Y.,: "Sensitivity Analysis in Functional Principal Component Analysis",
The 7th Japan-China Symposium on Statistics, Proceeding 37-40, Tokyo, Japan, Oct.29-31, 2000. [ps] [pdf]
Tanaka, Y., Watadani, S. and Yamanishi, Y.,: "On multiple-case diagnostics in multivariate method",
International Conference on Measurement and Multivariate Analysis (ICMMA), Proceedings 143-146, Banff, Canada, May11-14, 2000. [ps] [pdf]
Yamanishi, Y, Tanaka, Y.,: "Principal Component Analysis for Functional Data",
Joint Conference on Recent Developments in Statistics, Proceedings 31-35, Sockcho, Korea, Jan.7-9, 2000.

Posters at international conferences

Iwata, M. and Yamanishi, Y.,: "Simulation-guided elucidation of dynamic drug responses of the cellular systems",
The 21st European Conference on Computational Biology (ECCB2022), Barcelona, Spain, Sep.18-Sep.21, 2022.
Li, C., Yamanaka, C., Kaitoh, K. and Yamanishi, Y.,: "Transformer-Based Objective-Reinforced Generative Adversarial Network to Generate Desired Molecules",
The 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI 2022) (IJCAI-ECAI 2022), Vienna, Austria, Jul.23-Jul.29, 2022.
Kaitoh, K. and Yamanishi, Y.,: "Scaffold-Retained Structure Generator to Extensively Produce Molecules with Unique Chemical Substructures",
The 8th Strasbourg Summer School in Chemoinformatics (CS3-2022) (HP), Strasbourg, France, Jun.27-Jul.1, 2022.
Iwata, M. and Yamanishi, Y.,: "Simulation-guided elucidation of dynamic drug responses of the cellular systems",
The 8th Strasbourg Summer School in Chemoinformatics (CS3-2022) (HP), Strasbourg, France, Jun.27-Jul.1, 2022.
Berenger, F. and Yamanishi, Y.,: "A Distance-Based Boolean Applicability Domain for Classification of HTS Data",
OpenEye CUP XIX (HP), Santa Fe, France, Mar.3-Mar.5, 2019.
Yamanishi, Y., Pauwels, E., and Kotera, M.,: "Predicting drug side-effect profiles from the integration of chemical and biological spaces",
The 7th French-Japanese Workshop on Computational Methods in Chemistry (HP), Strasbourg, France, Jul.1-Jul.2, 2018.
Sawada, R., Iwata, M., Umezaki, M., Usui, Y., Kubono, T., Kadowaki, M., and Yamanishi, Y.,: Compuational analysis of the mode-of-action and pharmacological effects of Japanese Kampo medicines",
The Sixteenth Asia Pacific Bioinformatics Conference (APBC2018), Yokohama, Japan, Jan.15-Jan.17, 2018.
Iwata, M., Sawada, R., and Yamanishi, Y.,: Evaluation of computational methods for predicting drug efficacy and targets from drug-induced gene expression data in CMap",
International Symposium on Synthetic Systems Biology: Synthetic Metabolic Pathway, Mathematical System Analysis and Design of Bio-inspired System Joint 14th Symposium of Biochemical Systems Theory (BST2015), Fukuoka, Japan, Sep.17-18, 2015.
Sawada, R., Kotera, M., and Yamanishi, Y.,: Benchmarking a wide range of molecular descriptors for drug-target interaction prediction",
The 26th International Conference on Genome Informatics & The 14th International Conference on Bioinformatics (GIW/InCoB2015) (GIW/InCoB2015), Tokyo, Japan, Sep.9-11, 2015.
Iwata, M., Sawada, R., and Yamanishi, Y.,: Evaluation of computational methods for predicting drug efficacy and targets from drug-induced gene expression data in CMap",
The 26th International Conference on Genome Informatics & The 14th International Conference on Bioinformatics (GIW/InCoB2015) (GIW/InCoB2015), Tokyo, Japan, Sep.9-11, 2015.
Iwata, H., Sawada, R., Mizutani, S., and Yamanishi, Y.,: Systematic drug repositioning for a wide range of diseases by integrating phenotypic and molecular data",
The 23rd International Conference on Intelligent Systems for Molecular Biology & 14th European Conference on Computational Biology (ISMB/ECCB2015), Dublin, Ireland, Jul.10-Jul.14, 2015.
Yamanishi, Y., Kotera, M., Moriya, Y., Sawada, R., Kanehisa, M., and Goto, S.,: DINIES: A web-based application for predicting drug–target interaction networks",
The Thirteenth Asia Pacific Bioinformatics Conference (APBC2015), HsinChu, Taiwan, Jan.21-Jan.23, 2015.
Yamanishi, Y., Kotera, M., Moriya, Y., Sawada, R., Kanehisa, M., and Goto, S.,: DINIES: A web-based application for predicting drug–target interaction networks",
The 22nd International Conference on Intelligent Systems for Molecular Biology (ISMB2014), Boston, USA, Jul.11-Jul.15, 2014.
Iwata, H., Yoshihara, M., and Yamanishi, Y.,: "Computational drug repositioning by predicting drug-disease association network",
Annual Conference of Japanese Society for Bioinformatics (JSBi2013), Tokyo, 10/28-31, 2013.
Iwata, H., Yoshihara, M., and Yamanishi, Y.,: Predicting drug-disease association network toward drug repositioning",
Computational Biology Research Center Workshop 2013 (CBRC2013), Tokyo, 9/11-13, 2013. (Best Poster Award)
Yamanishi, Y., Pauwels, E., and Kotera, M.,: Predicting drug side-effect profiles from the integration of chemical and biological spaces",
The 21st International Conference on Intelligent Systems for Molecular Biology & 12th European Conference on Computational Biology (ISMB/ECCB2013), Berlin, Germany, Jul.19-Jul.23, 2013.
Takarabe, M., Kotera, M., Nishimura, Y., Goto, S., and Yamanishi, Y.,: Prediction of drug-target interaction network using FDA adverse event report system",
Pacific Symposium on Biocomputing 2013 (PSB2013), Hawaii, USA, Jan.3-Jan.7, 2013.
Kotera, M., Yamanishi, Y., Moriya, Y., Kanehisa, M., and Goto, S.,: GENIES: A web-based application for supervised gene network inference engine",
The 23rd Genome Informatics Workshop (GIW2012) (GIW2012), Tainan, Taiwan, Dec.11-Dec.14, 2012.
Yamanishi, Y., Pauwels, E., Saigo, H., and Stoven, V.,: Extracting chemogenomic features from drugtarget interaction network using sparse canonical correspondence analysis",
The 23rd Genome Informatics Workshop (GIW2012) (GIW2012), Tainan, Taiwan, Dec.11-Dec.14, 2012.
Yamanishi, Y.,: Predicting drugtarget interaction networks from the integration of chemical, genomic and pharmacological spaces",
Genomic Sciences Research Complex (GSC) Tanabata meeting (HP), Yokohama, Japan, Aug.25, 2011.
Sato, T., Yamanishi, Y., Ichihara, H., Kanehisa, M., and Toh, H.,: Comparison of Prediction Methods for Protein-Protein Interactions Using Co-Evolutionary Information",
The 16th International Conference on Genome Informatics (GIW2005), P080, Yokohama, Japan, Dec.19-21, 2005.
Hizukuri, Y., Yamanishi, Y., Nakamura, O., Yagi, F., Goto, S., and Kanehisa, M.,: Classification and Motif Extraction of Glycans in Bloods",
The 15th International Conference on Genome Informatics (GIW2004), P64, Yokohama, Japan, Dec.13-15, 2004.
Sato, T., Yamanishi, Y., Kanehisa, M., and Toh, H.,: Prediction of Protein-Protein Interactions Based on Real-Valued Phylogenetic Profiles Using Partial Correlation Coefficient",
The 15th International Conference on Genome Informatics (GIW2004), P122, Yokohama, Japan, Dec.13-15, 2004.
Yamanishi, Y., Sato, T., Vert, J.-P., Goto, S., and Kanehisa, M.,: Prediction of protein network and functions for yeast using multiple types of genomic data",
The Fourth International Workshop on Bioinformatics & Systems Biology (IBSB2004), pp.75-76, Kyoto, Japan, May 31-Jun.3, 2004.
Yamanishi, Y., Yoshizawa, A.C., Itoh, M., Katayama, T. and Kanehisa, M.,: Extraction of Organism Groups from Whole Genome Comparisons",
The 14th International Conference on Genome Informatics (GIW2003), pp.438-439, Yokohama, Japan, Dec.15-17, 2003.
Sato, T., Yamanishi, Y., Horimoto, K., Toh, H. and Kanehisa, M.,: Prediction of Protein-Protein Interactions from Phylogenetic Trees Using Partial Correlation Coefficient",
The 14th International Conference on Genome Informatics (GIW2003), pp.496-497, Yokohama, Japan, Dec.15-17, 2003.
Ota, K., Yamada, T., Yamanishi, Y., Goto, S. and Kanehisa, M.,: Comprehensive Analysis of Delay in Transcriptional Regulation using Expression Profiles",
The 14th International Conference on Genome Informatics (GIW2003), pp.302-303, Yokohama, Japan, Dec.15-17, 2003.
Yamada, T., Yamanishi, Y., Goto, S. and Kanehisa, M.,: Extraction of Modules from the Metabolic Pathways with Phylogenetic Profile",
The 13rd International Conference on Genome Informatics (GIW2002), pp.353-354, Tokyo, Japan, Dec.16-18, 2002.

Software

DINIES:: Drug-target Interaction Network Inference Engine based on Supervised Analysis
[link to DINIES]
GENIES:: Gene Network Inference Engine based on Supervised Analysis
[link to GENIES]
E-zyme:: EC Number Prediction System for Enzymatic Reactions in Metabolic Netowrk
[link to E-zyme]