Kei Taneishi, Researcher / Technical Staff

  • Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University
  • Health Navigation Team, Integrated Research Group, Compass to Healthy Life Research Complex Program and Biomedical and Pharmaceutical Data Intelligence Unit, Medical Sciences Innovation Hub Program, Cluster for Science and Technology Hub, RIKEN
  • Simulated Drug Discovery Group, Department of Molecular Life Science, Institute of Biomedical Research and Innovation, Foundation for Biomedical Research and Innovation

<my first name> (dot) <my last name> (at sign) riken (dot) jp


Japanese Articles


  • 1997-2001 Mathematics, Faculty of Science, Kyoto University.

Professional Work

  • 2017- Medical Sciences Innovation Hub Program, RIKEN
  • 2017- Compass to Healthy Life Research Complex Program, RIKEN
  • 2014- Simulated Drug Discovery Group, Foundation of Biomedical Research and Innovation
  • 2014-2017 Processor Research Team, Advanced Institute for Computational Science, RIKEN
  • 2009-2014 Department of Systems Bioscience for Drug Discovery, Kyoto University
  • 2005-2008 Department of Genomic Drug Discovery Science, Kyoto University

Professional Involvement

  • NVIDIA Deep Learning Institute University Ambassador at Kyoto University since 2018.
  • Task force and working group leader of Life Intelligence Consortium (LINC).
  • Tutorial course of K supercomputer-Based Drug Discovery project by Biogrid pharma consortium.
  • AI in Drug Discovery, Intel press briefing, 2017.
  • Introduction to Computational Life Sciences at Education Center on Computational Science and Engineering, Kobe University, 2017.
  • Co-organizer of Kyoto University AI Research Association seminar, since 2017.
  • Drug Discovery based on Deep Learning, Intel exhibition, ISC 2016.

Recent Research Contributions

Refereed Journal Publications

  1. Fujita, K., Taneishi, K., Inamoto, T., Ishizuya, Y., Takada, S., Tsujihata, M., Tanigawa, G., Minato, N., Nakazawa, S., Takada, T., Iwanishi, T., Uemura, M., Okuno, Y., Azuma, H., Nonomura, N. Adjuvant chemotherapy improves survival of patients with high-risk upper urinary tract urothelial carcinoma: a propensity score-matched analysis, BMC Urol. 2017 Dec 1;17(1):110.

  2. Uneno, Y., Taneishi, K., Kanai, M., Okamoto, K., Yamamoto, Y., Yoshioka, A., Hiramoto, S., Nozaki, A., Nishikawa, Y., Yamaguchi, D., Tomono, T., Nakatsui, M., Baba, M., Morita, T., Mataumoto, S., Kuroda, T., Okuno, Y., Muto, M. Development and validation of a set of six adaptable prognosis prediction (SAP) models based on time-series real-world big data analysis for patients with cancer receiving chemotherapy: A multicenter case crossover study, PloS One. 2017 Aug 24, 12(8). 2017.

  3. Hamanaka, M., Taneishi, K., Iwata, H., Ye, J., Pei, J., Hou, J., Okuno, Y. CGBVS‐DNN: prediction of compound‐protein Interactions based on deep learning, Molecular Informatics. 36(1-2), 2016.

  4. Nishikawa, Y., Kanai, M., Narahara, M., Tamon, A., Brown, J.B., Taneishi, K., Nakatsui, M., Okamoto, K., Uneno, Y., Yamaguchi, D., Tomono, T., Mori, Y., Matsumoto, S., Okuno, Y., Muto, M. Association between UGT1A1*28*28 genotype and lung cancer in the Japanese population, Int J Clin Oncol., 2016.

  5. Fazekas, S.Z., Ito, H., Okuno, Y., Seki, S., Taneishi, K. On computational complexity of graph inference from counting, Natural Computing, 12(4), 589-603, Special Issue, Dec 2013.

  6. Okuno, Y., Yang, JY., Taneishi, K., Yabuuchi, H., Tsujimoto, G. GLIDA: GPCR-ligand database for chemical genomic drug discovery, Nucleic Acids Research, 34 pp.D673-D677 Special Issue, Jan 1 2006.

Articles in Refereed Conference Proceedings

  1. Uchino, E., Taneishi, K., Sato, N., Yokoi, H., Okuno, Y., Yanagita, M. Automatic Detection of Global Sclerosis in Pathological Glomerular Images with Deep Neural Networks, ASN Kidney Week 2017.

  2. Uneno, Y., Baba, M., Kanai, M., Taneishi, K., Nakatsui, M., Okuno, Y., Muto, M., Morita, T. Validation of the Set of Six Adaptable Prognosis Prediction (SAP) Models for Cancer Patients in Palliative Care Settings : A sub analysis of the Japan-Prognostic assessment tools Validation (J-ProVal) study, ESMO Asia 2016.

  3. Hamanaka, M., Taneishi, K., Brown, J.B., Okuno, Y. Prediction of compound-protein interactions based on deep-layered learning, Pacifichem, Dec 2015.

  4. Yamaguchi, D., Taneishi, K., Tamon, A., Hamanaka, M., Brown, J.B., Mori, Y., Kanai, M., Matsumoto, S., Okuno, Y., Muto, M. Analysis of clinical practice data for predictive factors of neutrophil counts during weekly paclitaxel chemotherapy, European Cancer Congress, Vienna, Sep 2015.

  5. Uneno, Y., Taneishi, K., Kanai, M., Tamon, A., Okamoto, K., Nozaki, A., Yamaguchi, D., Nishikawa, Y., Tomono, T., Hamanaka, M., Brown, J.B., Matsumoto, S., Kuroda, T., Okuno, Y., Muto, M. Establishment of a terminal prognosis prediction model by applying time series analysis to real-world data, Annual Meeting of the Japanese Society of Medical Oncology, Sapporo, Jul 2015.

Refereed Books and Book Chapters

  1. The potential of AI in Drug Discovery, CICSJ Bulletin, Chemical Information and Computer Sciences, The Chemical Society of Japan, 2017.


  1. Okuno, Y., Taneishi, K., Tsujimoto, G. Estimation of protein-compound interaction and rational design of compound library based on chemical genomic information, PCT/JP2007/060736, US 2015.