論文・総説 - 長谷　武志

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  1. Arata Shirakami,Takeshi Hase,Yuki Yamaguchi,Masanori Shimono. Neural network embedding of functional microconnectome 2025.03; 9 (1): 159-180. ( DOI )

  2. Daisu Abe,Motoki Inaji,Takeshi Hase, et al.. A machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults Frontiers in Neurology. 2025.01; 15

  3. Mizuno, S; Noda, T; Mogushi, K; Hase, T; Iida, Y; Takeuchi, K; Ishiwata, Y; Uchida, S; Nagata, M. Prediction of Vancomycin-Associated Nephrotoxicity Based on the Area under the Concentration-Time Curve of Vancomycin: A Machine Learning Analysis BIOLOGICAL & PHARMACEUTICAL BULLETIN. 2024.11; 47 (11): 1946-1952. ( PubMed, DOI )

  4. Ryutaro Akiyoshi,Takeshi Has,Mayuri Sathiyananthavel, et al.. Noninvasive, label-free image approaches to predict multimodal molecular markers in pluripotency assessment Scientific Reports. 2024.07; 14

  5. Han, X; Bai, Z; Mogushi, K; Hase, T; Takeuchi, K; Iida, Y; Sumita, YI; Wakabayashi, N. Machine Learning Prediction of Tongue Pressure in Elderly Patients with Head and Neck Tumor: A Cross-Sectional Study JOURNAL OF CLINICAL MEDICINE. 2024.04; 13 (8): ( PubMed, DOI )

  6. Maruoka, H; Hattori, T; Hase, T; Takahashi, K; Ohara, M; Orimo, S; Yokota, T. Aberrant morphometric networks in Alzheimer's disease have hemispheric asymmetry and age dependence EUROPEAN JOURNAL OF NEUROSCIENCE. 2024.03; 59 (6): 1332-1347. ( PubMed, DOI )

  7. Hase Takeshi, Ghosh Samik, Aisaki Ken-ichi, Kitajima Satoshi, Kanno Jun, Kitano Hiroaki, Yachie Ayako. DTox: A deep neural network-based in visio lens for large scale toxicogenomics dat The Journal of Toxicological Sciences. 2024.03; 49 (1-3): 105-115. ( 医中誌 )

  8. Noda, T; Mizuno, S; Mogushi, K; Hase, T; Iida, Y; Takeuchi, K; Ishiwata, Y; Nagata, M. Development of a predictive model for nephrotoxicity during tacrolimus treatment using machine learning methods BRITISH JOURNAL OF CLINICAL PHARMACOLOGY. 2024.03; 90 (3): 675-683. ( PubMed, DOI )

  9. Kubo A, Masugi Y, Hase T, Nagashima K, Kawai Y, Takizawa M, Hishiki T, Shiota M, Wakui M, Kitagawa Y, Kabe Y, Sakamoto M, Yachie A, Hayashida T, Suematsu M.. Polysulfide Serves as a Hallmark of Desmoplastic Reaction to Differentially Diagnose Ductal Carcinoma In Situ and Invasive Breast Cancer by SERS Imaging Antioxidants . 2023.01; 12 (2): 240. ( PubMed, DOI )

  10. Abe D, Inaji M, Hase T, Takahashi S, Sakai R, Ayabe F, Tanaka Y, Otomo Y, Maehara T. A Prehospital Triage System to Detect Traumatic Intracranial Hemorrhage Using Machine Learning Algorithms. JAMA network open. 2022.06; 5 (6): e2216393. ( PubMed, DOI )

  11. Polouliakh N, Hase T, Ghosh S, Kitano H. Toxicity Analysis of Pentachlorophenol Data with a Bioinformatics Tool Set. Methods in molecular biology (Clifton, N.J.). 2022; 2486 105-125. ( PubMed, DOI )

  12. Katsuda T, Sato N, Mogushi K, Hase T, Muramatsu M. Sub-GOFA: A tool for Sub-Gene Ontology function analysis in clonal mosaicism using semantic (logical) similarity. Bioinformation. 2022; 18 (1): 53-60. ( PubMed, DOI )

  13. (*Takeshi Hase is a part of, FANTOM consortium), Grapotte M, Saraswat M, Bessière C, Menichelli C, Ramilowski JA, Severin J, Hayashizaki Y, Itoh M, Tagami M, Murata M, Kojima-Ishiyama M, Noma S, Noguchi S, Kasukawa T, Hasegawa A, Suzuki H, Nishiyori-Sueki H, Frith MC, * FANTOM consortium, Chatelain C, Carninci P, de Hoon MJL, Wasserman WW, Bréhélin L, Lecellier CH. Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network Nature Communications. 2021.06; 12, 3297. ( DOI )

  14. Nishimura T, Nakamura H, Yachie A, Hase T, Fujii K, Koizumi H, Naruki S, Takagi M, Matsuoka Y, Furuya N, Kato H, Saji H. Disease-related cellular protein networks differentially affected under different EGFR mutations in lung adenocarcinoma. Scientific reports. 2020.12; 10 (1): 10881. ( PubMed, DOI )

  15. (Book Chapter) "Chapter 13. Cancer Network Medicine". - Network Medicine: Complex Systems in Human Disease and Therapeutics (Joseph Loscalzo, Albert-László Barabási, and Edwin K. Silverman (Eds.)) 2017.12; 294-323. ( DOI )

  16. Caron E, Roncagalli R, Hase T, Wolski WE, Choi M, Menoita MG, Durand S, García-Blesa A, Fierro-Monti I, Sajic T, Heusel M, Weiss T, Malissen M, Schlapbach R, Collins BC, Ghosh S, Kitano H, Aebersold R, Malissen B, Gstaiger M. Precise Temporal Profiling of Signaling Complexes in Primary Cells Using SWATH Mass Spectrometry. Cell reports. 2017.03; 18 (13): 3219-3226. ( PubMed, DOI )

  17. Hu Y, Hase T, Li HP, Prabhakar S, Kitano H, Ng SK, Ghosh S, Wee LJ. A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data. BMC genomics. 2016.12; 17 (Suppl 13): 1025-29. ( PubMed, DOI )

  18. Effect of placebo and lorazepam on functional connectivity in fearful vocal processing: an fMRI study 2016.06; 19 (Suppl_1): 54. ( DOI )

  19. Inferring causal molecular networks: empirical assessment through a community-based effort 2016.04; 13 (4): 310-318. ( DOI )

  20. Hase T, Kikuchi K, Ghosh S, Kitano H, Tanaka H. A computational approach to prioritize drug-target genes in the human protein interaction network The proceedings of The 21st International Symposium on Artificial Life and Robotics 2016 (AROB 21st 2016). 2016.01; 871-876.

  21. Kawakami E, Singh VK, Matsubara K, Ishii T, Matsuoka Y, Hase T, Kulkarni P, Siddiqui K, Kodilkar J, Danve N, Subramanian I, Katoh M, Shimizu-Yoshida Y, Ghosh S, Jere A, Kitano H. Network analyses based on comprehensive molecular interaction maps reveal robust control structures in yeast stress response pathways. NPJ systems biology and applications. 2016; 2 15018. ( PubMed, DOI )

  22. Hase T, Kikuchi K, Ghosh S, Kitano H, Tanaka H. Controllability of protein-protein interaction networks and their relationships with drug-targets, essential genes, and degree connectivities. The proceedings of The 20th International Symposium on Artificial Life and Robotics 2015 (AROB 20th 2015). 2015.01; 813-818.

  23. Takeshi Hase, Kaito Kikuchi, Samik Ghosh, Hiroaki Kitano, Hiroshi Tanaka. (Research article) Identification of drug-target modules in the human protein–protein interaction network Artificial Life and Robotics. 2014.12; 19 (4): 406-413. ( DOI )

  24. Matsuoka Y, Matsumae H, Katoh M, Eisfeld AJ, Neumann G, Hase T, Ghosh S, Shoemaker JE, Lopes TJ, Watanabe T, Watanabe S, Fukuyama S, Kitano H, Kawaoka Y. A comprehensive map of the influenza A virus replication cycle. BMC systems biology. 2013.10; 7 (97): 97. ( PubMed, DOI )

  25. Hase T, Ghosh S, Yamanaka R, Kitano H. Harnessing diversity towards the reconstructing of large scale gene regulatory networks. PLoS computational biology. 2013; 9 (11): e1003361. ( PubMed, DOI )

  26. (Book Chapter) "Chapter 20. Protein-Protein Interaction Networks: Structures, Evolution, and Application to Drug Design" - Protein-Protein Interactions - Computational and Experimental Tools (W. Cai, H. Hong (eds.)) 2012.03; 405-426. ( DOI )

  27. Hanada K, Hase T, Toyoda T, Shinozaki K, Okamoto M. Origin and evolution of genes related to ABA metabolism and its signaling pathways. Journal of plant research. 2011.07; 124 (4): 455-65. ( PubMed, DOI )

  28. Hase T, Niimura Y, Tanaka H. Difference in gene duplicability may explain the difference in overall structure of protein-protein interaction networks among eukaryotes. BMC evolutionary biology. 2010.11; 10 358. ( PubMed, DOI )

  29. Hase T, Tanaka H, Suzuki Y, Nakagawa S, Kitano H. Structure of protein interaction networks and their implications on drug design. PLoS computational biology. 2009.10; 5 (10): e1000550. ( PubMed, DOI )

  30. Hase T, Niimura Y, Kaminuma T, Tanaka H. Non-uniform survival rate of heterodimerization links in the evolution of the yeast protein-protein interaction network. PloS one. 2008.02; 3 (2): e1667. ( PubMed, DOI )

  31. 長谷 武志, 荻島 創一, 中川 草, 田中 博. 1P301 タンパク質間相互作用ネットワークのトポロジー構造における構造決定因子について(数理生物学)) 生物物理. 2005; 45 S107. ( DOI )

  32. Tsuji Shingo, Aburatani Hiroyuki, Hase Takeshi, Tanaka Hiroshi. Applications of graph neural networks for drug-repositioning and cancer research CANCER SCIENCE. 2022.02; 113 1790.

  33. Shirakami Arata, Toba Takuma, Nakamoto Isao, Hase Takeshi, Shimono Masanori. Deep neural embedding of neuronal connectivity JOURNAL OF COMPUTATIONAL NEUROSCIENCE. 2021.12; 49 (SUPPL 1): S172-S173.

  34. Tsuji Shingo, Hase Takeshi, Yachie-Kinoshita Ayako, Nishino Taiko, Ghosh Samik, Kikuchi Masataka, Shimokawa Kazuro, Aburatani Hiroyuki, Kitano Hiroaki, Tanaka Hiroshi. Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer's disease ALZHEIMERS RESEARCH & THERAPY. 2021.05; 13 (1): 92. ( PubMed, DOI )

  35. 長谷 武志, 谷内江 綾子, 辻 真吾. AI とBig Data におけるDrug Discovery（創薬）への可能性 ICUとCCU. 2019.06; 43 (4): 191-198.

  36. Ackerman Emily E., Alcorn John F., Hase Takeshi, Shoemaker Jason E.. A dual controllability analysis of influenza virus-host protein-protein interaction networks for antiviral drug target discovery BMC BIOINFORMATICS. 2019.06; 20 (1): 297. ( PubMed, DOI )

  37. Tsuji Shingo, Hase Takeshi, Tanaka Hiroshi, Aburatani Hiroyuki. Al technology based drug discovery method with protein interaction network for cancer drug repositioning CANCER SCIENCE. 2018.01; 109 786.

  38. 肥田 道彦, 長谷 武志, 濱 智子, 池田 裕美子, 八幡 憲明, 舘野 周, 高橋 英彦, 松浦 雅人, 鈴木 秀典, 大久保 善朗. 恐怖音声認知・脳処理時の機能的結合に対するプラセボ・ロラゼパムの効果 機能的MRI研究 日本神経精神薬理学会年会プログラム・抄録集. 2016.07; 46回 215. ( 医中誌 )

  39. 長谷 武志, 荻島 創一, 鈴木 泰博, 中川 草, 田中 博. 1P297 タンパク質間相互作用ネットワークの進化における疎密構造と自己結合するタンパク質の持つ相互作用数の頻度分布との関係(数理生物学) 生物物理. 2004; 44 S104. ( DOI )

  40. 長谷 武志, 鈴木 泰博, 荻島 創一, 田中 博. タンパク質間相互作用ネットワークの枝数による階層構造 生物物理. 2003; 43 S244. ( DOI )

  41. Hiroaki Kitano, Takeshi Hase. (Issue image) Cloud topology in the yeast protein interaction network (PLoS Computational Biology Issue Image | Vol. 5(10) October 2009, see also Hase et al. doi:10.1371/journal.pcbi.1000550). PLoS Computational Biology. 2009; 5 (10): ev05.i10. ( DOI )

  42. Ogishima S, Hase T, Nakagawa S, Suzuki Y, Tanaka H. (proceedings) Molecular Evolutionary Analysis of Yeast Protein Interaction Network World Academy of Science, Engineering and Technology （proceedings). 2005; 11 69-72.

  43. Suzuki Y, S Nakagawa, T Hase, S Ogishima, H Tanaka. (proceedings) The Specificity of Topology of the Protein-Protein Interactions in Yeast and Its Biological Characteristics Journal of the Mass Spectrometry Society of Japan (proceedings). 2005; 53 (3): 137-141. ( DOI )

  44. 中川草, 鈴木泰博, 長谷武志, 荻島創一, 田中博. (proceedings) 酵母のタンパク質相互作用の細胞内局在ごとの分析 平成16年度数理モデル化と問題解決シンポジウム 情報処理学会（proceedings). 2004; 361-366.

  45. Nakagawa S, Ogishima S, Hase T, Suzuki Y, Tanaka H. (proceedins)The analysis between functions and densities in yeast intercomplex protein-protein interactions The Fifteenth International conference on Genome Informatics (proceedings). 2004; 131-1,2.

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