Kei Taneishi, Researcher / Visiting Engineer

  • Cluster for Science and Technology Hub, RIKEN

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

Education

  • Mathematics, Faculty of Science, Kyoto University, 1997-2002.

Professional Work

  • Medical Sciences Innovation Hub Program, RIKEN, 2017-2018.
  • Graduate School of Medicine, Kyoto University, 2014-2018.
  • Simulated Drug Discovery Group, Foundation of Biomedical Research and Innovation, 2014-2018.
  • Advanced Institute for Computational Science, RIKEN, 2014-2017.
  • Graduate School of Pharmaceutical Sciences, Kyoto University, 2005-2014.

Professional Involvement

  • NVIDIA Deep Learning Institute University Ambassador at Kyoto University, 2018.
  • Life Intelligence Consortium, 2017-2018.
  • K supercomputer-Based Drug Discovery project, 2016, 2018.
  • Introduction to Computational Life Sciences at Education Center on Computational Science and Engineering, Kobe University, 2017.
  • Kyoto University AI Research Association, 2017-2018.

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 Urology, 17(1):110, Dec 1 2017.

  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, 12(8), Aug 24 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.

Articles in Refereed Conference Proceedings

  1. Tsuchiya, Y., Taneishi, K., YonezawaY., Effects of single mutations on STING activation, CBI Annual meeting, Tokyo, 2018.

  2. 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, New Orleans, 2017.

  3. 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, Singapore, 2016.

  4. Yamaguchi, D., Taneishi, K., Hamanaka, M., Mori, Y., Kanai, M., Matsumoto, S., Okuno, Y., Muto, M., Analysis of predictive factors for variation of neutrophil count during gastrointestinal cancer chemotherapy using Real World Data, UEG Week, Vienna, 2016.

  5. Kitajima, T., Taneishi, K., Iwata, H., Kamijima, H., Yamamoto, S., Inari, K., Okuno, Y., Application of deep learning for large scale data with standard performance workstation(s), CBI Annual Meeting, Tokyo, 2016.

  6. Uneno, Y., Taneishi, K., Kanai, M., Okamoto, K., Yamamoto, Y., Yoshioka, A., Nozaki, A., Matsumoto, S., Okuno, Y., Muto, M., Development of a Prognosis Prediction Model Involving Time Series Real-World Big Data Analysis for Patients with Cancer Receiving Chemotherapy, Annual Meeting of the Japanese Society of Medical Oncology, Kobe, 2016.

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

  8. 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.

  9. 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.

  10. Hamanaka, M., Taneishi, K., Iwata, H., Okuno, Y., Prediction of compound-protein interactions based on deep learning methods, 38th Symposium on Chemoinformatics, Tokyo, 2015.

Other Publications

  1. Interview about AI in Drug Discovery, Fole, みずほ総合研究所, Oct, 2018.

  2. Nagao, C., Taneishi, K., 人工知能(AI)がもたらす創薬イノベーション, 医薬ジャーナル, 2018.

  3. Taneishi, K., マテリアルズ・インフォマティクス, 情報機構, 2018.

  4. ゲノム医療における人工知能(AI)の活用, インテル, 2018.

  5. 創薬における人工知能(AI)の活用, インテル, 2018.

  6. Tokuhisa, A., Taneishi, K., Okuno, Y., AI導入によるバイオテクノロジーの発展, シーエムシー出版, 2018.

  7. Taneishi, K., ディープラーニングを用いた創薬ビッグデータの解析技術, 技術情報, 2018.

  8. Taneishi, K., Iwata, H., Kojima, R., Okuno, Y., 創薬とAIの良好な関係, 実験医学別冊, 2017.

  9. Uchino, E., Taneishi, K., Nakatsui, M., Kamada, M., Araki, M., Okuno, Y., Real-world Clinical Data and Genome: A New Perspective on Clinical Big Data Analysis, 生体医工学, 55(4), 173-182, 2017.

  10. Taneishi, K., Iwata, H., Kojima, R., Okuno, Y., 創薬におけるAIの可能性, CICSJ Bulletin, 35(3), 202, 2017.

  11. Nakatsui, M., Taneishi, K., Okuno, Y., 医療ビッグデータ解析による実臨床からの生命科学展開, 実験医学, 35(1), 40-45, 2017.

  12. Taneishi, K., Iwata, H., Okuno, Y., 人工知能・機械学習・ディープラーニング関連技術とその応用, 情報機構, 2016.

Other Presentations

  1. Taneishi, K., Practice of Drug Discovery in AI on HPC environment, Intel Developer Conference, 2018.

  2. Taneishi, K., Optimization of Deep Learning for Drug Discovery, CBI Annual Meeting, Tokyo, 2016.

  3. Taneishi, K., Drug Discovery based on Deep Learning, International Supercomputer Conference, 2016.

Patents

  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.