講演・口頭発表等 - 長谷　武志

分割表示  40 件中 1 - 20 件目  ／  全件表示 >>

  1. Takeshi Hase. Artificial intelligence based drug-target repositioning across diverse diseases. 7th International Symposium on BioComplexity 2022.01.25

  2. 辻 真吾, 油谷 浩幸, 長谷 武志, 田中 博. 創薬と癌研究のためのGraph Neural Networkの応用. 日本癌学会総会記事 2021.09.01

  3. AI and network analysis based computational framework for drug-target repositioning. 2021.01.23

  4. Takeshi Hase, Masanori Shimono. Neural network embedding of real neuronal networks. NETsciX 2020 2020.01.20

  5. Takeshi Hase, Samik Ghosh, Ken-ichi Aisaki, Satoshi Kitajima, Jun Kanno, Hiroaki Kitano. (招待講演)DTOX: Deep neural network-based computational framework to analyze omics data in Toxicology. OPENTOX ASIA 2018 2018.05.25 Asahi Seimei Otemachi Building, Tokyo, Japan

  6. 長谷 武志. (招待講演) Lecture 05: Application of machine learning methods to drug discovery. 1st Big Data Machine Learning in Healthcare in Japan 2018.02.25

  7. Takashi Hase, Shingo Tsuji, Kazuro Shimokawa, Hiroshi Tanaka. (Peer reviewed) Application of Deep Learning to Drug Discovery. Workshop on Artificial Life and Robotics in Busan 2017.09.08

  8. 辻 真吾, 長谷 武志, 田中 博, 油谷 浩幸. タンパク相互作用ネットワークを用いた新しいがん治療のためのAI創薬. 日本癌学会総会記事 2017.09.01

  9. Takeshi Hase, Samik Ghosh, Ayako Yachie, Hiroaki Kitano. (Peer reviewed) A neural network based text mining approach for inference of protein-protein interaction networks.. 2nd International Symposium on BioComplexity 2017.01.20 B-Con PLAZA, Beppu, JAPAN

  10. Yongli Hu, Takeshi Hase, Huipeng Li, Shyam Prabhakar, Hiroaki Kitano, See Kiong Ng, Samik Ghosh, Lawrence Jin, Kiat Wee. (Peer-reviewed) A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data.. 15th International Conference on Bioinformatics (InCOB 2016) 2016.09.21 Singapore

  11. Takeshi Hase, Kaito Kikuchi, Samik Ghosh, Hiroshi Tanaka, Hiroaki Kitano. (Peer reviewed) A computational approach to prioritize drug-target genes in the human protein interaction network. 1st International Symposium on BioComplexity 2016.01.21 B-Con PLAZA, Beppu, JAPAN

  12. Takeshi Hase, Kaito Kikuchi, Samik Ghosh, Hiroaki Kitano, Hiroshi Tanaka. (peer-reviewed) Controllability of protein-protein interaction networks and their relationships with drug-targets, essential genes, and degree connectivities. International Symposium on Artificial Life and Robotics AROB 20th 2015 2015.01.21

  13. Kaito Kikuchi, Takeshi Hase, Samik Ghosh, Hiroaki Kitano. (peer-reviewed) A network guided approach towards identification of novel drug targets in MRSA. 8th Asian Young Researchers Conference on Computational and Omics Biology (AYRCOB 2015.01.19

  14. Takeshi Hase. Identification of drug-target modules in the human protein–protein interaction network. AROB 19th 2014.01.22 Oita, Japan

  15. Takeshi Hase, Kaito Kikuchi, Samik Ghosh, Hiroaki Kitano, Hiroshi Tanaka. (peer-reviewed) Identification of drug-target modules in the human protein–protein interaction network. International Symposium on Artificial Life and Robotics 2014.01.01 B-Con Plaza, Beppu, Japan

  16. 一般口演, Takeshi Hase, Yoshihito Niimura. (Peer-reviewed) Difference in gene duplicability may explain the difference in overall structure of protein-protein interaction networks among eukaryotes.. Society for Molecular Biology and Evolution 2012 2012.06.01 Dublin Ireland

  17. 野田 円, 水野 正太郎, 茂櫛 薫, 長谷 武志, 飯田 頼嗣, 竹内 勝之, 石渡 泰芳, 永田 将司. タクロリムスによる腎機能障害の発現を患者個別に予測する機械学習モデルの構築. 日本臨床薬理学会学術総会抄録集 2024.01.01

  18. 阿部 大数, 稲次 基希, 長谷 武志, 田中 洋次, 前原 健寿. 頭部外傷の予後予測・診断　機械学習を用いたTalk and Deteriorate症例の予測　Think FAST registryを用いた解析. 日本脳神経外傷学会プログラム・抄録集 2023.02.01

  19. 阿部 大数, 稲次 基希, 長谷 武志, 大友 康裕, 田中 洋次, 前原 健寿. 脳神経外科救急におけるデジタル技術の活用　機械学習を用いた外傷性頭蓋内出血患者の搬送前トリアージシステム. Neurosurgical Emergency 2023.02.01

  20. 長谷 武志, 西野 泰子, Sathiyananthavel Mayuri, Rajendiran Ramanathan, 大場 雅宏, 高木 浩輔, 小林 茂, 合田 和史, 堀 邦夫, 葉梨 拓哉, 秋吉 皓太, 鈴木 浩文, 谷内江 綾子. 画像AIによる薬物性肝障害の病理診断と遺伝子発現パターン予測の統合. 日本毒性病理学会講演要旨集 2023.01.01

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