論文・総説 - 長谷　武志

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.