Kei Taneishi, Researcher / Technical Staff

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

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Education

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

Professional Work

  • Medical Sciences Innovation Hub Program, RIKEN, 2017-.
  • Compass to Healthy Life Research Complex Program, RIKEN, 2015-.
  • Simulated Drug Discovery Group, Foundation of Biomedical Research and Innovation, 2014-2018.
  • Processor Research Team, Advanced Institute for Computational Science, RIKEN, 2014-2017.
  • Department of Systems Bioscience for Drug Discovery, Kyoto University, 2009-2014.
  • Department of Genomic Drug Discovery Science, Kyoto University, 2005-2009.

Professional Involvement

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

  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, Singapore, 2016.

  3. Fujita, K., Taneishi, K., Inamoto, T., Ishizuya, Y., Takada, S., Tsujihata, M., Tanigawa, G., Minato, N., Nakazawa, S., Takada, T., Koida, Y., Uemura, M., Okuno, Y., Azuma, H., Nonomura, N., 高リスク上部尿路上皮癌に対する術後補助化学療法の効果及び効果予測因子の検討, 第54回日本癌治療学会, Yokohama, 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., ディープラーニングに基づく化合物とタンパク質の相互作用予測, 第38回ケモインフォマティクス討論会, Tokyo, 2015.

  11. Hamanaka, M., Taneishi, K., Okuno, Y., 深層学習に基づくタンパク質と化合物の相互作用予測, 情報処理学会第77回全国大会, Kyoto, 2015.

  12. Muto, M., Kanai, M., Mori, Y., Matsumoto, S., Sakuma, T., Koyanagi, T., Kuroda, T., Morita, S., Kosugi, S., Tamon, A., Taneishi, K., Okuno, Y., Takaori, A., 京大病院におけるバイオバンク, クリニカルシークエンス, ビッグデータ解析の取組, 第53回日本癌治療学会学術集会, Kyoto, 2015.

  13. Feng, CL., Araki, M., Taneishi, K., Okuno, Y., システム創薬のためのケミカル-バイオ-クリニカル統合データベース, 日本薬学会第132年会, Sapporo, 2012.

Other Publications

  1. 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.

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

  3. 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.

  4. 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.

  5. Taneishi, K., Tokuhisa, A., Okuno, Y., ビッグデータの創薬への応用, 遥か, 11, 2018.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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., AIが拓く創薬イノベーション, AI Experience, Tokyo, 2018.

  2. Taneishi, K., HPC環境における創薬AI開発の実践, Intel Developer Conference, 2018.

  3. Taneishi, K., AI創薬にかかる期待, Intel Press Briefing, 2017.

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

  5. 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.