Original Article

• 2024)

  • Igarashi Y, Hasegawa A, Matsumoto S, Iwata H, Kojima R, Okuno Y, Yamada H, Constructing a graph neural network-based artificial intelligence model to predict drug-induced phospholipidosis potential, Fundamental Toxicological Sciences, 11(6):279-288, 2024/11/22, doi:10.2131/fts.11.279
  • Toda Y, Fujita H, Mino K, Koyama T, Matsuoka S, Kaizuka T, Agawa M, Matsumoto S, Idei A, Nishikori M, Okuno Y, Osada H, Yoshida M, Takaori-Kondo A, Iwai K, Synergistic involvement of the NZF domains of the LUBAC accessory subunits HOIL-1L and SHARPIN in the regulation of LUBAC function, Cell Death & Disease, 15(11):813, 2024/11/11, doi:10.1038/s41419-024-07199-z
  • Sasagasako T, Ueda A, Mineharu Y, Mochizuki Y, Doi S, Park S, Terada Y, Sano N, Tanji M, Arakawa Y, Okuno Y, Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features, PLoS One, 11;19(11):e0303002, 2024/11/11, doi:10.1371/journal.pone.0303002
  • Yamaji K, Kanenawa K, Morofuji T, Nishikawa R, Imada K, Kohjitani H, Watanabe H, Tazaki J, Taniwaki M, Koga S, Akashi R, Kubo S, Ohya M, Kikuchi T, Ohira H, Numasawa Y, Arikawa M, Iwama M, Kitai T, Kobayashi Y, Shiomi H, Tada T, Yamaji Y, Daidoji H, Ohtani H, Furukawa Y, Kadota K, Toyofuku M, Ando K, Ono K, Kimura T; PREDICTOR investigators, Serial Optical Coherence Tomography Assessment of Coronary Atherosclerosis and Long-Term Clinical Outcomes., J Am Heart Assoc., 13(21):e034458, 2024/10/22, doi:10.1161/JAHA.123.034458
  • Mitsugu A, Toru E, Kazuhiro T, Shigeyuki M, Yunoshin T, Hironori K, Gert-Jan B, Tsutomu Y, Yuta I, Yukari S, Narutoshi K, Mitsunori I, Yasushi O, Molecular Dynamics Unveils Multiple-Site Binding of Inhibitors with Reduced Activity on the Surface of Dihydrofolate Reductase, Journal of the American Chemical Society, 2024/10/12, In press, doi:10.1021/jacs.4c04648
  • Tomoki S, Yohei M, Takeshi F, Yasutaka F, Hideo C, Silsu P, Kota N, Yasuzumi M, Masakazu O, Takayuki K, Yoshiki A, RNF213 Mutation Associated with the Progression from Middle Cerebral Artery Steno-Occlusive Disease to Moyamoya Disease, Translational stroke research, Advance online publication, 2024/8/27, doi:10.1007/s12975-024-01293-2
  • Hosoya K, Ozasa H, Tanji M, Yoshida H, Ajimizu H, Tsuji T, Yoshida H, Terada Y, Sano N, Mineharu Y, et al., Performance status improvement and advances in systemic treatment after brain metastases resection: a retrospective single-center cohort study of non-small cell lung cancer patients, BMC Cancer, 2024;24(1):1030, 2024/8/21, doi:10.1186/s12885-024-12798-2
  • Cun Y, Guo W, Ma B, Okuno Y, Wang J. Decoding the specificity of m6A RNA methylation and its implication in cancer therapy. Molecular therapy : the journal of the American Society of Gene Therapy, 32(8):2461-2469, 2024/8/7, doi:10.1016/j.ymthe.2024.05.035
  • Nishiwaki S, Shizuta S, Inoue T, Morinaga A, Yoneda F, Tanaka M, Aizawa T, Kohjitani H, Ono K, Prevalence and characteristics of atrioventricular nodal reentrant tachycardia with a bystander concealed nodoventricular/nodofascicular pathway, Heart Rhythm, S1547-5271(24)02823-6,  2024/6/26, doi:10.1016/j.hrthm.2024.06.049
  • Taisei Tosaki, Eiichiro Uchino, Ryosuke Kojima, Yohei Mineharu, Mikio Arita, Nobuyuki Miyai, Yoshinori Tamada, Tatsuya Mikami, Koichi Murashita, Shigeyuki Nakaji, Yasushi Okuno, Out-of-distribution Reject Option Method for Dataset Shift Problem in Early Disease Onset Prediction. arXiv.org. 2024;09:14:01, 2024/5/30, doi.org/10.48550/arXiv.2405.19864
  • Suzuki, M., Mineharu, Y., Okawa, M., Yoshida, K., Nagata, M., Yang, T., Suzuki, K., Takayama, N., Yamamoto, Y., Tabara, Y., Miyamoto, S., Arakawa, Y., & Matsuda, F. (2024). Common and distinct risk profiles of asymptomatic extra- and intracranial atherosclerosis in the Nagahama cohort, Journal of Stroke and Cerebrovascular Diseases, 33(8), 107782.Advance online publication, 2024/5/20, doi:10.1016/j.jstrokecerebrovasdis.2024.107782
  • Tanaka M, Kohjitani H, Yamamoto E, Morimoto T, Kato T, Yaku H, Inuzuka Y, Tamaki Y, Ozasa N, Seko Y, Shiba M, Yoshikawa Y, Yamashita Y, Kitai T, Taniguchi R, Iguchi M, Nagao K, Kawai T, Komasa A, Kawase Y, Morinaga T, Toyofuku M, Furukawa Y, Ando K, Kadota K, Sato Y, Kuwahara K, Okuno Y, Kimura T, Ono K; KCHF Study Investigators., Development of interpretable machine learning models to predict in-hospital prognosis of acute heart failure patients, ESC Heart Fail., Advance online publication, 2024/5/15, doi:10.1002/ehf2.14834
  • Bekker GJ, Oshima K, Araki M, Okuno Y, Kamiya N, CBinding Mechanism between Platelet Glycoprotein and Cyclic Peptide Elucidated by McMD-Based Dynamic Docking, Journal of Chemical Information and Modeling, 2024;64(10):4158-4167, 2024/5/15, doi:10.1021/acs.jcim.4c00100
  • Nishiwaki S, Shizuta S, Kohjitani H, Ono K, Insight from the microelectrodes in case of two different types of premature ventricular contractions originating from left ventricular summit, Indian Pacing and Electrophysiology Journal, 2024;24(4):212-216, 2024/5/8, doi:10.1016/j.ipej.2024.05.001
  • Inoue M, Ekimoto T, Yamane T, Ikeguchi M, Computational Analysis of Activation of Dimerized Epidermal Growth Factor Receptor Kinase Using the String Method and Markov State Model, Journal of chemical information and modeling, 2024;64(9):3884-3895, 2024/4/26, doi:10.1021/acs.jcim.4c00172
  • Ikeda, S., Sakata, A., Arakawa, Y., Mineharu, Y., Makino, Y., Takeuchi, Y., Fushimi, Y., Okuchi, S., Nakajima, S., Otani, S., & Nakamoto, Y., Clinical and imaging characteristics of supratentorial glioma with IDH2 mutation, Neuroradiology, 2024;66(6):973-981, 2024/4/24, doi:10.1007/s00234-024-03361-8
  • Mori S, Izumi H, Araki M, Liu J, Tanaka Y, Kagawa Y, Sagae Y, Ma B, Isaka Y, Sasakura Y, Kumagai S, Sakae Y, Tanaka K, Shibata Y, Udagawa H, Matsumoto S, Yoh K, Okuno Y, Goto K, Kobayashi SS., LTK mutations responsible for resistance to lorlatinib in non-small cell lung cancer harboring CLIP1-LTK fusion, Communications biology, 2024;7(1):412, 2024/4/4, doi:10.1038/s42003-024-06116-6
  • Hiroaki Iwata*, Yoshihiro Hayashi*, Takuto Koyama, Aki Hasegawa, Kosuke Ohgi, Ippei Kobayashi, Yasushi Okuno, Feature extraction of particle morphologies of pharmaceutical excipients from scanning electron microscope images using convolutional neural networks, International Journal of Pharmaceutics, 22024;653:123873, 2024/3/25, doi:10.1016/j.ijpharm.2024.123873
  • Sakuragi M, Uchino E, Sato N, Matsubara T, Ueda A, Mineharu Y, Kojima R, Yanagita M, Okuno Y, Interpretable machine learning-based individual analysis of acute kidney injury in immune checkpoint inhibitor therapy, PLoS One, 19(3):e0298673, 2024/3/19, doi:10.1371/journal.pone.0298673
  • Kawata M, Yonezawa A, Mineharu Y, Itohara K, Mizota T, Matsui Y, Kikuchi T, Yamao Y, Hattori EY, Hamada M, Hira D, Furukawa K, Miyamoto S, Terada T, Matsubara K, Arakawa Y. Development of extended pharmacokinetic models for propofol based on measured blood and brain concentrations, Scientific Reports, 14(1), 6326, 2024/3/15, doi:10.1038/s41598-024-56863-z
  • Igarashi Y, Kojima R, Matsumoto S, Iwata H, Okuno Y, Yamada H, Developing a GNN-based AI model to predict mitochondrial toxicity using the bagging method, The Journal of Toxicological Sciences, 2024;49(3):117-126, 2024/3/1, doi:10.2131/jts.49.117
  • Suzuki M, Uchibori K, Oh-Hara T, Nomura Y, Suzuki R, Takemoto A, Araki M, Matsumoto S, Sagae Y, Kukimoto-Niino M, Kawase Y, Shirouzu M, Okuno Y, Nishio M, Fujita N, Katayama R, A macrocyclic kinase inhibitor overcomes triple resistant mutations in EGFR-positive lung cancer, NPJ Precis Oncol., 2024;8(1):46, 2024/2/23, doi:10.1038/s41698-024-00542-9
  • Tomoto M, Mineharu Y, Sato N, Tamada Y, Nogami-Itoh M, Kuroda M, Adachi J, Takeda Y, Mizuguchi K, Kumanogoh A, Natsume-Kitatani Y, & Okuno Y. Idiopathic pulmonary fibrosis-specific Bayesian network integrating extracellular vesicle proteome and clinical information.,Scientific Reports, 2024;14:1315. 2024/1/15, doi.org/10.1038/s41598-023-50905-8
  • Kohjitani H, Koshimizu H, Nakamura K, & Okuno Y, Recent developments in machine learning modeling methods for hypertension treatment.,Hypertens. Res. , 1–8 (2024) 2024/1/12, doi:10.1038/s41440-023-01547-w.
  • Ryo Kanada*, Atsushi Tokuhisa*, Yusuke Nagasaka, Shingo Okuno, Koichiro Amemiya, Shuntaro Chiba, Gert-Jan Bekker, Narutoshi Kamiya, Koichiro Kato, and Yasushi Okuno, Enhanced Coarse-Grained Molecular Dynamics Simulation with a Smoothed Hybrid Potential Using a Neural Network Model, Journal of Chemical Theory and Computation, 2024;20(1):7-17, 2024/1/9, doi:10.1021/acs.jctc.3c00889
• 2023)

  • Mineharu Y, Takagi Y, Koizumi A, Morimoto T, Funaki T, Hishikawa T, Araki Y, Hasegawa H, Takahashi JC, Kuroda S, Houkin K, Miyamoto S; SUPRA Japan Study Group. Posterior cerebral artery involvement in unilateral moyamoya disease is exclusively ipsilateral and influenced by RNF213 mutation gene dose: The SUPRA Japan study: PCA involvement in unilateral moyamoya.,Journal of Stroke Cerebrovasc. Dis., 2023;33:107513.　2023/12/22 doi/10.1016/j.jstrokecerebrovasdis.2023.107513
  • Shimizu, Y., Ohta, M., Ishida, S., Terayama, K., Osawa, M., Honma, T., Ikeda, K., AI-driven molecular generation of not-patented pharmaceutical compounds using world open patent data.,Journal of Cheminformatics , 15, 120 (2023), 2023/12/13, doi.org/10.1186/s13321-023-00791-z
  • Okamoto R，Kojima R， Nakatsui M , Toward AI-supported evaluation for safety control measures against near-miss events in pharmaceutical products , Safety Science , Volume 168,106314 , 2023/12/1 , doi.org/10.1016/j.ssci.2023.106314
  • Iwata H, Nakai T, Koyama T, Matsumoto S, Kojima R, Okuno Y, VGAE-MCTS: a New Molecular Generative Model combining Variational Graph Auto-Encoder and Monte Carlo Tree Search.,Journal of chemical information and modeling , 63.23: 7392-7400., 2023/11/22, doi/10.1021/acs.jcim.3c01220
  • Yamane T, Nakayama T, Ekimoto T, Inoue M, Ikezaki K, Sekiguchi H, Kuramochi M, Terao Y, Judai K, Saito M, Ikeguchi M, C. Sasaki Y , Comparison of the Molecular Motility of Tubulin Dimeric Isoforms: Molecular Dynamics Simulations and Diffracted X-ray Tracking Study , International Journal of Molecular Sciences , 2,415,423 , 2023/10/21 , https://www.mdpi.com/1422-0067/24/20/15423
  • Hatano N, Kamada M, Kojima R, Okuno Y. , Network-based prediction approach for cancer-specific driver missense mutations using a graph neural network. ,  BMC bioinformatics, 24(1),383 , 2023/10/10 , https://doi.org/10.1186/s12859-023-05507-6
  • Yamazaki K, Wada Y, Tokuhisa A, Wada M, Katoh T, Umeda Y, Okuno Y, Nakagawa A, An Auto-Encoder to Reconstruct Structure with Cryo-EM Images via Theoretically Guaranteed Isometric Latent Space, and Its Application for Automatically Computing the Conformational Pathway., Lecture Notes in Computer Science(including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14220 LNCS, 394-404, 2023/10/1, doi: 10.1007/978-3-031-43907-0_38
  • Echizen H, Hanaoka K, Shimamoto K, Hibi R, Toma-Fukai S, Ohno H, Sasaki E, Komatsu T, Ueno T, Tsuchiya Y, Watanabe Y, Otsuka T, Saito H, Nagatoishi S, Tsumoto K, Kojima H, Okabe T, Shimizu T, Urano Y , Discovery of a cystathionine γ-lyase (CSE) selective inhibitor targeting active-site pyridoxal 5′-phosphate (PLP) via Schiff base formation , Scientific Reports, 1,316,456 , 2023/9/30 ,  https://doi.org/10.1038/s41598-023-43536-6
  • Hattori EY, Arakawa Y, Mineharu Y, Furukawa K, Terada Y, Yamao Y, Tanji M, Kikuchi T, Miyamoto S. , Seizure control by adding on other anti-seizure medication on seizure during levetiracetam administration in patients with glioma-related epilepsy. , BMC Cancer , 23, 849 , 2023/9/11 , doi.org/10.1186/s12885-023-11273-8
  • Matsuoka T, Hattori A, Oishi S, Araki M, Biao Ma, Fujii T, Arichi N, Okuno Y, Kakeya H, Yamasaki S, Ohno H, Inuki S , Establishment of an MR1 Presentation Reporter Screening System and Identification of Phenylpropanoid Derivatives as MR1 Ligands. , Journal of medicinal chemistry , 66(17),12520–12535 , 2023/8/23 , doi.org/10.1021/acs.jmedchem.3c01122
  • Nakamura K, Uchino E, Sato N, Araki A, Terayama K, Kojima R, Murashita K, Itoh K, Mikami T, Tamada Y, Okuno Y , Individual health-disease phase diagrams for disease prevention based on machine learning , Journal of Biomedical Informatics , 144,104448 , 2023/8/23 , doi.org/10.1016/j.jbi.2023.104448
  • Nojima S#*, Kadoi T, Suzuki A, Kato C, Ishida S, Kido K, Fujita K, Okuno Y, M. Hirokawa, K. Terayama*, Morii E, , Deep-learning-based differential diagnosis of follicular thyroid tumors using histopathological images , Modern Pathology , 100296,2023/7/31, DOI: 10.1016/j.modpat.2023.100296
  • Aya Nakamura, Ryosuke Kojima, Yuji Okamoto, Eiichiro Uchino, Yohei Mineharu, Yohei Harada, Mayumi Kamada, Manabu Muto, Motoko Yanagita, Yasushi Okuno , A New Deep State-Space Analysis Framework for Patient Latent State Estimation and Classification from EHR Time Series Data. , arXiv:2307.11487 , 2023/7/21 , https://doi.org/10.48550/arXiv.2307.11487
  • Koyama T, Matsumoto S, Iwata H, Kojima R, Okuno Y. , Improving Compound–Protein Interaction Prediction by Self-Training with Augmenting Negative Samples , Journal of Chemical Information and Modeling , 63, 15 , 4552–4559 , 2023/7/17 , https://pubs.acs.org/doi/full/10.1021/acs.jcim.3c00269
  • Shimizu S, Kondo J, Onuma K, Coppo R, Ota K, Kamada M, Harada H, Tanaka Y, Adachi Nakazawa M, Tamada Y, Okuno Y, Kawada K, Kazutaka Obama, Robert J. Coffey, Yoshiyuki Fujiwara, Masahiro Inoue , Inhibition of the bone morphogenetic protein pathway suppresses tumor growth through downregulation of epidermal growth factor receptor in MEK/ERK-dependent colorectal cancer , Cancer Science ,2023/6/25 , doi: 10.1111/cas.15882
  • Zhu A,  Chiba S,  Shimizu Y, Kunitake K,  Okuno Y, Aoki Y, Yokota T , Ensemble-Learning and Feature Selection Techniques for Enhanced Antisense Oligonucleotide Efficacy Prediction in Exon Skipping , Pharmaceutics , 15(7),1808 , 2023/6/24 , doi.org/10.3390/pharmaceutics15071808
  • Sato N, Mori KP, Sakai K, Miyata H, Yamamoto S, Kobayashi T, Haga H, Yanagita M, Okuno Y, Exploring the mechanism of BK polyomavirus-associated nephropathy through consensus gene network approach, PLoS One, 2023;18(6):e0282534, 2023/6/15, doi:10.1371/journal.pone.0282534
  • Iwata H, Application of In Silico Technologies for Drug Target Discovery and Pharmacokinetic Analysis , Chemical and Pharmaceutical Bulletin ,2023/6/1 , doi:10.1248/cpb.c22-00638
  • Wang Y, Fushimi Y, Arakawa Y, Shimizu Y, Sano K, Sakata A, Nakajima S, Okuchi S, Hinoda T, Oshima S, Otani S, Ishimori T, Tanji M, Mineharu Y, Yoshida K, & Nakamoto Y , Evaluation of isocitrate dehydrogenase mutation in 2021 world health organization classification grade 3 and 4 glioma adult-type diffuse gliomas with 18F-fluoromisonidazole PET.  , Japanese Journal of Radiology ,  2023/5/23 , doi:10.1007/s11604-023-01450-x
  • Matsumoto S, Ishida S, Terayama K, Okuno Y, Quantitative analysis of protein dynamics using a deep learning technique combined with experimental cryo-EM density data and MD simulations, Biophysics and physicobiology, 2023;20(2):e200022, 2023/5/16, doi:10.2142/biophysico.bppb-v20.0022
  • Mimoto T, Yokoyama H, Nakamura T, Isohara T, Hashimoto M, Kojima R, Hasegawa A, Okuno Y, Privacy-Preserving Correlation Coefficient, IEICE Transactions on Information and Systems, E106.D(5):868-876, 2023/5/1, https://doi.org/10.1587/transinf.2022DAP0014
  • Gert-Jan Bekker, Araki M, Oshima K, Okuno Y, Kamiya N , Mutual induced-fit mechanism drives binding between intrinsically disordered Bim and cryptic binding site of Bcl-xL , Communications Biology , 6(1), 349, 2023/3/30, doi: 10.1038/s42003-023-04720-6
  • Gao J, Makiyama T, Yamamoto Y, Kobayashi T, Aoki H, Maurissen TL, Wuriyanghai Y, Kashiwa A, Imamura T, Aizawa T, Huang H, Kohjitani H, Nishikawa M, Chonabayashi K, Fukuyama M, Manabe H, Nakau K, Wada T, Kato K, Toyoda F, Yoshida Y, Makita N, Woltjen K, Ohno S, Kurebayashi N, Murayama T, Sakurai T, Horie M, Kimura T. , Novel Calmodulin Variant p.E46K Associated With Severe Catecholaminergic Polymorphic Ventricular Tachycardia Produces Robust Arrhythmogenicity in Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes. , Circ Arrhythm Electrophysiol  , 16(3), e011387 , 2023/3/3, doi: 10.1161/CIRCEP.122.011387
  • Aizawa T, Wada Y, Hasegawa K, Huang H, Imamura T, Gao J, Kashiwa A, Kohjitani H, Fukuyama M, Kato K, Kato ET, Hisamatsu T, Ohno S, Makiyama T, Kimura T, Horie M. , Non-missense variants of KCNH2 show better outcomes in type 2 long QT syndrome.  , EP Europace , 25(4), 1491-1499, 2023/3/2, doi: 10.1093/europace/euac269
  • Igarashi Y, Re S, Kojima R, Okuno Y, Yamada H, Development of a GCN-based model to predict in vitro phototoxicity from the chemical structure and HOMO-LUMO gap, Japanese Society of Toxicology, 2023;48(5):243-249, 2023/3/1, doi:10.2131/jts.48.243
  • Torizuka D, Uto M, Umeda K, Kamitori T, Iwai A, Saida S, Kato I, Mineharu Y, Arakawa Y, Takita J, et al. , A Scalp Dose Threshold for Preventing Permanent Alopecia in Scalp-Avoidance Whole-Brain Irradiation With Volumetric Modulated Arc Radiation Therapy for Pediatric Patients With Medulloblastomas. , Int. J. Radiat. Oncol. Biol. Phys. , S0360-3016(23)00069-X , 2023/1/26, doi: 10.1016/j.ijrobp.2023.01.027
  • Sasagasako T, Mori H, Hattori EY, Ikedo T, Hamano E, Shimonaga K, Kushi Y, Iihara K, Kataoka H, Radiation-Induced Changes Associated with Obliteration of Brain AVMs after Repeat Radiosurgery, American Journal of Neuroradiology January, 44(2):143-149, 2023/1/26, doi:10.3174/ajnr.A7772
  • Harada Y, Sato A, Nakamura H, Kai K, Kitamura S, Nakamura T, Kurihara Y, Ikeda S, Sueoka E, Kimura S and Naoko Sueoka‐Aragane, Anti-cancer effect of afatinib, dual inhibitor of HER2 and EGFR, on novel mutation HER2 E401G in models of patient-derived cancer. , BMC Cancer. , 23(1):77. , 2023/1/23, doi: 10.1186/s12885-022-10428-3
  • Roberto Coppo, Jumpei Kondo, Keita Iida, Mariko Okada, Kunishige Onuma, Yoshihisa Tanaka, Mayumi Kamada, Masayuki Ohue, Kenji Kawada, Kazutaka, Obama, Masahiro Inoue , Distinct but interchangeable subpopulations of colorectal cancer cells with different growth fates and drug sensitivity. , iScience , 26(2):105962, 2023/1/13 , https://doi.org/10.1016/j.isci.2023.105962
• 2022)

  • Nishiwaki S, Watanabe S, Yoneda F, Tanaka M, Aizawa T, Yamagami S, Komasa A, Kawaji T, Yoshizawa T,Kohjitani H, MorimotoT, Kimura T, Shizuta S.  , Impact of catheter ablation on functional tricuspid regurgitation in patients with atrial fibrillation.  , Journal of Interventional Cardiac Electrophysiology ,1–13 (2022) ,  2022/12/13 , doi:10.1007/s10840-022-01410-x.
  • Nishiwaki S, Naka M, Morinaga A, Yoneda F, Tanaka M, Kohjitani H, Shizuta S. , An irregularly irregular supraventricular tachycardia: What is the mechanism? , Journal of Cardiovasc Electrophysiology , 2022/12/13 , https://doi.org/10.1111/jce.15781
  • Washio T, Xiaoke Cui, Kanada R, Okada J, Sugiura S, Okuno Y, Takada S, Hisada T, Using incomplete Cholesky factorization to increase the time step in molecular dynamics simulations., Journal of Computational and Applied Mathematics, 415:114519, 2022/12/1, doi:10.1016/j.cam.2022.114519
  • Yoshizawa T, Ishida S*, Sato T, Ohta M, Honma T, and Terayama K* , Selective Inhibitor Design for Kinase Homologs Using Multiobjective Monte Carlo Tree Search , Journal of Chemical Information and Modeling , 62(22):5351-5360, 2022/11/28, https://doi.org/10.1021/acs.jcim.2c00787
  • Mineharu, Y., Nakamura, Y., Sato, N., Kamata, T., Oichi, Y., Fujitani, T., Funaki, T., Okuno, Y., Miyamoto, S., Koizumi, A., & Harada, K. H. (2022). , Increased abundance of Ruminococcus gnavus in gut microbiota is associated with moyamoya disease and non-moyamoya intracranial large artery disease.  , Scientific Reports , 12(1),20244 ,  2022/11/24 , doi:10.1038/s41598-022-24496-9
  • Kohjitani H, Koda S, Himeno Y, Makiyama T, Yamamoto Y, Yoshinaga D, Wuriyanghai Y, Kashiwa A, Toyoda F, Zhang Y, Amano A,  Noma A&  Kimura T , Gradient-based parameter optimization method to determine membrane ionic current composition in human induced pluripotent stem cell-derived cardiomyocytes , Scientific Reports  , 12(1):19110, 2022/11/9 , doi: 10.1038/s41598-022-23398-0
  • Ueda M, Usami K, Yamao Y, Yamawaki R, Umaba C, Liang N, Nankaku M, Mineharu Y, Honda M, Hitomi T, Ikeguchi R, Ikeda A, Miyamoto S, Matsuda S, Arakawa Y, Correlation between brain functional connectivity and neurocognitive function in patients with left frontal glioma , Scientific Reports, volume 12,18302 ,  2022/11/8 , https://doi.org/10.1038/s41598-022-22493-6
  • Ohsuga T, Egawa M, Kii M, Ikeda Y, Ueda A, Chigusa Y, Mogami H, Mandai M.,Association between non-anemic iron deficiency in early pregnancy and perinatal mental health: A retrospective pilot study., THE JOURNAL OF Obstet Gynaecol Research ,Nov;48(11):2730-2737 , 2022/11, https://doi.org/10.1111/jog.15397
  • Nishi M, Uchino E, Okuno Y, Matoba S, Robust prognostic prediction model developed with integrated biological markers for acute myocardial infarction, PLOS ONE , 17(11):e0277260, 2022/11/3, doi: 10.1371/journal.pone.0277260
  • Iwata H, Hayashi Y, Hasegawa A, Terayama K, Okuno Y, Classification of scanning electron microscope images of pharmaceutical excipients using deep convolutional neural networks with transfer learning , International Journal of Pharmaceutics , 4:100135, 2022/10/18, doi: 10.1016/j.ijpx.2022.100135
  • Tabata J, Nakaoku T, Araki M, Yoshino R, Kohsaka S, Otsuka A, Ikegami M, Ui A, Kanno SI, Miyoshi K, Matsumoto S, Sagae Y, Yasui A, Sekijima M, Mano H, Okuno Y, Okamoto A, Kohno T, Novel Calcium-Binding Ablating Mutations Induce Constitutive RET Activity and Drive Tumorigenesis，Cancer research, 82(20):3751-3762, 2022/10/17, doi: 10.1158/0008-5472.CAN-22-0834
  • Okamoto Y, Hirano M, Morino K, Kajita MK, Nakaoka S, Tsuda M, Sugimoto KJ, Tamaki S, Hisatake J, Yokoyama H, Igarashi T, Shinagawa A, Sugawara T, Hara S, Fujikawa K, Shimizu S, Yujiri T, Wakita H, Nishiwaki K, Tojo A, Aihara K, Early dynamics of chronic myeloid leukemia on nilotinib predicts deep molecular response. , npj Systems Biology and Applications , 8(1):39, 2022/10/13 , doi: 10.1038/s41540-022-00248-3
  • Ryosuke Kojima and Yuji Okamoto , Learning Deep Input-Output Stable Dynamics , Neural Information Processing Systems 2022 , arXiv:2206.13093 , 2022/10/3 , ” https://doi.org/10.48550/arXiv.2206.13093″
  • Ueda A, Watari H, Mandai M, Fukuhara S, Sugitani Y, Ogino K, Kamijima S, Enomoto T. , Incidence of gastrointestinal perforation associated with bevacizumab in combination with neoadjuvant chemotherapy as first-line treatment of advanced ovarian, fallopian tube, or peritoneal cancer: analysis of a Japanese healthcare claims database. , Journal of Gynecolgic Oncology ,  Nov;33(6):e78 ,  2022/9/30 , doi: 10.3802/jgo.2022.33.e78
  • Funaki T, Miyakoshi A, Kataoka H, Takahashi JC, Takagi Y, Yoshida K, Kikuchi T, Mineharu Y, Okawa M, Yamao Y, Fushimi Y, Miyamoto S, Larger Posterior Revascularization Associated with Reduction of Choroidal Anastomosis in Moyamoya Disease: A Quantitative Angiographic Analysis. , American Journal of Neuroradiology , 43,1279–85,2022/9/, doi: 10.3174/ajnr.A7609
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  • Arakawa Y, Mineharu Y, Uto M, Mizowaki T. , Optimal managements of elderly patients with glioblastoma, Japanese Journal of Clinical Oncology, 52(8):833-842, 2022/5/12, doi:10.1093/jjco/hyac075
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  • Nakamura Y, Mineharu Y*, Kamata T, Funaki T, Miyamoto S, Koizumi A, Harada KH, Lack of Association between Seropositivity of Vasculopathy-Related Viruses and Moyamoya Disease, Journal of Stroke and Cerebrovascular Diseases, 31(7):106509, 2022/4/29 , doi: 10.1016/j.jstrokecerebrovasdis.2022.106509 
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  • Oichi Y, Mineharu Y, Agawa Y, Morimoto T, Funaki T, Hitomi T, Kobayashi H, Todo K, Tani S, Imamura H, Yoshida K, Kataoka H, Koizumi A, Sakai N, Miyamoto S. , Characterization of Moyamoya and Middle Cerebral Artery Diseases by Carotid Canal Diameter and RNF213 p.R4810K Genotype. J Stroke Cerebrovasc Dis. , 14;31(6) , 106481 , 2022/4/14, DOI: 10.1016/j.jstrokecerebrovasdis.2022.106481
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  • Yamashita H, Arakawa Y, Terada Y, Takeuchi Y, Mineharu Y, Sumiyoshi S, Tokunaga S, Nakajima K, Kawabata N, Tanaka K, et al. , Whole-genome sequencing analysis of an atypical teratoid/rhabdoid tumor in a patient with Phelan-McDermid syndrome: a case report and systematic review. , Brain Tumor Pathol,, 2022/1/24, DOI: 10.1007/s10014-022-00440-7
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  • Sato N, Uchino E, Kojima R, Sakuragi M, Hiragi S, Minamiguchi S, Haga H, Yokoi H, Yanagita M, Okuno Y , Evaluation of Kidney Histological Images Using Unsupervised Deep Learning. Kidney international reports ,6(9), 2445－2454 , 2021/9 , DOI: 10.1016/j.ekir.2021.06.008
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  • Mizota T, Hamada M, Shiraki A, Kikuchi T, Mineharu Y, Yamao Y, Hattori EY, Yonezawa A, Furukawa K, Arakawa Y. Factors associated with somnolence during brain function mapping in awake craniotomy. J. Clin. Neurosci. ,89:349–353., 2021, https://doi.org/10.1016/j.jocn.2021.05.050
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  • Shuntaro Chiba, Aki Tanabe, Makoto Nakakido, Yasushi Okuno, Kouhei Tsumoto, Masateru Ohta. “Structure-based design and discovery of novel anti-tissue factor antibodies with cooperative double-point mutations, using interaction analysis” Sci Rep. 2020;10:17590. Available from: http://doi.org/10.1038/s41598-020-74545-4
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  • Masuda N, Murakami K, Kita Y, Hamada A, Kamada M, Teramoto Y, Sakatani T, Matsumoto K, Sano T, Saito R, Okuno Y, Ogawa O, Kobayashi T. “Trp53 mutation in Krt5-expressing basal cells facilitates the development of basal squamous-like invasive bladder cancer in the chemical carcinogenesis of mouse bladder” Am J Pathol. 2020 Apr 24. Available from: https://doi.org/10.1016/j.ajpath.2020.04.005
  • Kanada R, Tokuhisa A, Tsuda K, Okuno Y, Terayama K. “Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning” Biomolecules. 2020;10(3):482. Available from: https://doi.org/10.3390/biom10030482
  • Saito Y, Koya J, Araki M, Kogure Y, Shingaki S, Tabata M, McClure M, Yoshifuji K, Matsumoto S, Isaka Y, Tanaka H, Kanai T, Miyano S, Shiraishi Y, Okuno Y, Kataoka K. . “Landscape and function of multiple mutations within individual oncogenes” Nature. 2020. Available from: https://doi.org/10.1038/s41586-020-2175-2
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  • Hatae R, Chamoto K, Kim YH, Sonomura K, Taneishi K, Kawaguchi S, Yoshida H, Ozasa H, Sakamori Y, Akrami M, Fagarasan S, Masuda I, Okuno Y, Matsuda F, Hirai T, Honjo T. “Combination of host immune metabolic biomarkers for the PD-1 blockade cancer immunotherapy” JCI Insight. 2020;5(2):e133501. Aveilable from: https://doi.org/10.1172/jci.insight.133501
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• 2019)

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  • Kita Y, Hamada A, Saito R, Teramoto Y, Tanaka R, Takano K, Nakayama K, Murakami K, Matsumoto K, Akamatsu S, Yamasaki T, Inoue T, Tabata Y, Okuno Y, Ogawa O, Kobayashi T. “Systematic chemical screening identifies disulfiram as a repurposed drug that enhances sensitivity to cisplatin in bladder cancer: a summary of preclinical studies” Br J Cancer 2019;121(12):1027-38. Available from: https://doi.org/10.1038/s41416-019-0609-0
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  • Ikeda, A., Funakoshi, E., Araki, M., Ma, B., Karuo, Y., Tarui, A., Sato, K., Okuno, Y., Kawai, K., Omote, M. “Structural modification of indomethacin toward selective inhibition of COX-2 with a significant increase in van der Waals contributions” Bioorg Med Chem. 2019;27(9):1789-1794. Avalilable from: https://doi.org/10.1016/j.bmc.2019.03.021
    
  • Okada, K., Araki, M., Sakashita, T., Ma, B., Kanada, R., Yanagitani, N., Horiike, A., Koike, S., Oh-hara, T., Watanabe, K., Tamai, K., Maemondo, M., Nishio, M., Ishikawa, T., Okuno, Y., Fujita, N., Katayama, R. “Prediction of ALK mutations mediating ALK-TKIs resistance and drug re-purposing to overcome the resistance” EBioMedicine. 2019;41:105-119. Available from: https://doi.org/10.1016/j.ebiom.2019.01.019
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  • Bekker, G.J., Araki, M., Oshima, K., Okuno, Y., Kamiya, N. “Dynamic Docking of a Medium-Sized Molecule to Its Receptor by Multicanonical MD Simulations” J. Phys. Chem. B. 2019;123(11):2479-2490. Available from: https://doi.org/10.1021/acs.jpcb.8b12419
    
  • Mizumoto, A., Ohashi, S., Kamada, M., Saito, T., Nakai, Y., Baba, K., Hirohashi, K., Mitani, Y., Kikuchi, O., Matsubara, J., Yamada, A., Takahashi, T., Lee, H., Okuno, Y., Kanai, M., and Muto, M. “Combination treatment with highly bioavailable curcumin and NQO1 inhibitor exhibits potent antitumor effects on esophageal squamous cell carcinoma” J Gastroenterol. 2019;54:687-698. Available from: https://doi.org/10.1007/s00535-019-01549-x
  • Yamada, K., Sato, H., Sakamaki, K., Kamada, M., Okuno, Y., Fukuishi, N., Furuta, K., and Tanaka, S. “Suppression of IgE-Independent Degranulation of Murine Connective Tissue-Type Mast cells by Dexamethasone” Cells. 2019;8(2):E112. Available from: https://doi.org/10.3390/cells8020112
    
  • Terayama, K., Tamura, R., Nose, Y., Hiramatsu, H., Hosono, H., Okuno, Y., Tsuda, K. “Efficient Construction Method for Phase Diagrams Using Uncertainty Sampling” Phys Rev Mater, accepted on 26 Jan, 2019. Available from: https://doi.org/10.1103/PhysRevMaterials.3.033802
• 2018)

  • Kou T, Kana M, Kamada M, Nakatsui M, Matsumoto S, Okuno Y, Muto M. “A Platform for Comprehensive Genomic Profiling in Human Cancers and Pharmacogenomics Therapy Selection” Methods in molecular biology (Clifton, N.J.). 2018;1825:413-424. Available from: https://doi.org/10.1007/978-1-4939-8639-2_14
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  • Terayama K, Iwata H, Araki M, Okuno Y, Tsuda K. “Machine learning accelerates MD-based binding pose prediction between ligands and proteins” Bioinformatics. 2018;34(5):770-778. Available from: https://doi.org/10.1093/bioinformatics/btx638
  • Iwata Y, Katayama Y, Okuno Y, Wakabayashi S. “Novel inhibitor candidates of TRPV2 prevent damage of dystrophic myocytes and ameliorate against dilated cardiomyopathy in a hamster model” Oncotarget. 2018;9(18):14042-14057. Available from: https://doi.org/10.18632/oncotarget.24449
  • Kondo T, Kanai M, Kou T, Sakuma T, Mochizuki H, Kamada M, Nakatsui M, Uza N, Kodama Y, Masui T, Takaori K, Matsumoto S, Miyake H, Okuno Y, Muto M. “Association between homologous recombination repair gene mutations and response to oxaliplatin in pancreatic cancer” Oncotarget. 2018;9(28):19817-19825. Available from: https://doi.org/10.18632/oncotarget.24865
  • Noda Y, Kuzuya A, Tanigawa K, Araki M, Kawai R, Ma B, Sasakura Y, Maesako M, Tashiro Y, Miyamoto M, Uemura K, Okuno Y, Kinoshita A. “Fibronectin type Ⅲ domain-containing protein 5 interacts with APP and decreases amyloid β production in Alzheimer’s disease” Molecular Brain. 2018;11:61. Available from: https://doi.org/10.1186/s13041-018-0401-8
  • Araki M, Iwata H, Ma B, Fujita A, Terayama K, Sagae Y, Ono F, Tsuda K, Kamiya N, Okuno Y. “Improving the accuracy of protein-ligand binding mode prediction using a molecular dynamics-based pocket generation approach” J. Comput. Chem. 2018;39(32):2679-2689. Available from: https://doi.org/10.1002/jcc.25715
  • Terayama K, Yamashita T, Oguchi T, Tsuda K. “Fine-grained optimization method for crystal structure prediction” npj Computational Materials, 2018;4(32). Available from: https://doi.org/10.1038/s41524-018-0090-y
  • Tamada Y. “Memory Efficient Parallel Algorithm for Optimal DAG Structure Search using Direct Communication” Journal of Parallel and Distributed Computing”, 2018;119:27-35. Available from: https://doi.org/10.1016/j.jpdc.2018.03.011
  • Kawai R, Araki M, Yoshimura M, Kamiya N, Ono M, Saji H, Okuno Y. “Core Binding Site of a Thioflavin-T-Derived Imaging Probe on Amyloid β Fibrils Predicted by Computational Methods” ACS Chem. Neurosci 2018;9(5):957-966. Available from: https://doi.org/10.1021/acschemneuro.7b00389
  • Nakaoku T, Kohno T, Araki M, Niho S, Chauhan R, Knowles P.P,  Tsuchihara K, Matsumoto S, Shimada Y, Mimaki S, Ishii G, Ichikawa H, Nagatoishi S, Tsumoto K, Okuno Y, Yoh K, McDonald N.Q, Goto K. “A secondary RET mutation in the activation loop conferring resistance to vandetanib” Nature Communications, 2018;9(1):625. Available from: https://doi.org/10.1038/s41467-018-02994-7
• 2017)

  • Nakatsui M, Kamada M, Araki M, Okuno Y. “In silico drug discovery by supercomputer “K”” Nihon yakurigaku zasshi.Folia pharmacologica Japonica. 2017;149(6):281-287. Available from: https://doi.org/10.1254/fpj.149.281
  • Schneider G, Funatsu K, Okuno Y, Winkler D. “De novo Drug Design – Ye olde Scoring Problem Revisited” Molecular informatics. 2017;36(1-2). Available from: https://doi.org/10.1002/minf.201681031
  • Kondo T, Kanai M, Kou T, Sakuma T, Mochizuki H, Kamada M, Nakatsui M, Uza N, Kodama Y, Masui T, Takaori K, Matsumoto S, Miyake H, Okuno Y, Muto M. “Impact of BRCAness on the efficacy of oxaliplatin-based chemotherapy in patients with unresectable pancreatic cancer” JOURNAL OF CLINICAL ONCOLOGY. 2017;35(4). Available from: https://doi.org/10.1200/JCO.2017.35.4_suppl.250
  • Nakayama T, Imanaka Y, Okuno Y, Kato G, Kuroda T, Goto R, Tanaka S, Tamura H, Fukuhara S, Fukuma S, Muto M, Yanagita M, Yamamoto Y. “Analysis of the evidence-practice gap to facilitate proper medical care for the elderly: investigation, using databases, of utilization measures for National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB)” Environmental health and preventive medicine. 2017;22(1):51. Available from: https://doi.org/10.1186/s12199-017-0644-5
  • Morita K, Suzuki K, Maeda S, Matsuo A, Mitsuda Y, Tokushige C, Kashiwazaki G, Taniguchi J, Maeda R, Noura M, Hirata M, Kataoka T, Yano A, Yamada Y, Kiyose H, Tokumasu M, Matsuo H, Tanaka S, Okuno Y, Muto M, Naka K, Ito K, Kitamura T, Kaneda Y, Liu PP, Bando T, Adachi S, Sugiyama H, Kamikubo Y. “Genetic regulation of the RUNX transcription factor family has antitumor effects” The Journal of clinical investigation. 2017;127(7):2815-2828. Available from: https://doi.org/10.1172/JCI91788
  • 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, Norio N. “Adjuvant chemotherapy improves survival of patients with high-risk upper urinary tract urothelial carcinoma: a propensity score-matched analysis” BMC Urol. 2017;17(1):110. Available from: https://doi.org/10.1186/s12894-017-0305-4
  • Murakami R, Matsumura N, Brown, J.B, Higasa K, Tsutsumi T, Kamada M, Abou-Taleb H, Hosoe Y, Kitamura S, Yamaguchi K, Abiko K, Hamanishi J, Baba T, Koshiyama M, Okuno Y, Yamada R, Matsuda F, Konishi I, Mandai M. “Exome Sequencing Landscape Analysis in Ovarian Clear Cell Carcinoma Shed Light on Key Chromosomal Regions and Mutation Gene Networks” Am J Pathol. 2017;187:2246-2258. Available from: https://doi.org/10.1016/j.ajpath.2017.06.012
  • Terayama, K., Iwata, H., Araki, M., Okuno, Y., Tsuda, K. “Machine Learning Accelerates MD-based Binding-Pose Prediction between Ligands and Proteins” Bioinformatics. 2018;34(5):770-778. Available from: https://doi.org/10.1093/bioinformatics/btx638
  • 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, Matsumoto 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. 2017;12(8). Available from: https://doi.org/10.1371/journal.pone.0183291
  • Kiyan, W., Ito, A., Nakagawa, Y., Mukai, S., Mori, K., Arai, T., Uchino E, Okuno Y, Kuroki, H. “Relationships Between Quantitative Pulse-Echo Ultrasound Parameters from the Superficial Zone of the Human Articular Cartilage and Changes in Surface Roughness, Collagen Content or Collagen Orientation Caused by Early Degeneration” Ultrasound.Med.Biol. 2017;43(8):1703-1715. Available from: https://doi.org/10.1016/j.ultrasmedbio.2017.03.015
  • Bekker G, Kamiya N, Araki M, Fukuda I, Okuno Y, Nakamura H. “Accurate prediction of complex structure and affinity for a flexible protein receptor and its inhibitor” J. Chem. Theory Comput., 2017;13(6):2389-2399. Available from: https://doi.org/10.1021/acs.jctc.6b01127
  • Kou T, Kanai M, Yamamoto Y, Kamada M, Nakatsui M, Sakuma T, Mochizuki H, Hiroshima A, Sugiyama A, Nakamura E, Miyake H, Minamiguchi S, Takaori K, Matsumoto S, Haga H, Seno H, Kosugi S, Okuno Y, Muto M. “Clinical sequencing using a next-generation sequencing-based multiplex gene assay in patients with advanced solid tumors” Cancer Science, 2017;108(7):1440-1446. Available from: https://doi.org/10.1111/cas.13265
  • Uchibori K, Inase N, Araki M, Kamada M, Sato S, Okuno Y, Fujita N, Katayama R. “Brigatinib combined with anti-EGFR antibody overcomes osimertinib resistance in EGFR-mutated non-small-cell lung cancer” Nature Communications. 2017;8:14768. Available from: https://doi.org/10.1038/ncomms14768
  • Nakaoku T, Kohno T, Araki M, Niho S, Chauhan R, Knowles P.P,  Tsuchihara K, Matsumoto S, Shimada Y, Mimaki S, Ishii G, Ichikawa H, Nagatoishi S, Tsumoto K, Okuno Y, Yoh K, McDonald N.Q, Goto K. “A secondary RET mutation in the activation loop conferring resistance to vandetanib” Nature Communications, 2018;9(1):625. Available from: https://doi.org/10.1038/s41467-018-02994-7
• 2016)

  • Hamanaka M, Taneishi Kei, Iwata H, Ye J, Pei J, Hou J, Okuno, Y. “CGBVS-DNN: prediction of compound-protein Interactions based on deep learning” Mol. Inf. 2016;36(1-2). Available from: https://doi.org/10.1002/minf.201600045
  • Araki M, Kamiya N, Sato M, Nakatsui M, Hirokawa T, Okuno Y, “The effect of conformational flexibility on binding free energy estimation between kinases and their Inhibitors” Journal of Chemical Information and Modeling. 2016;56(12):2445-2456.Available from: https://doi.org/10.1021/acs.jcim.6b00398
  • 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;22(2):269-273. Available from: https://doi.org/10.1007/s10147-016-1061-2
• 2015)

  • Kikuchi O, Ohashi S, Nakai Y, Nakagawa S, Matsuoka K, Kobunai T, Takechi T, Amanuma Y, Yoshioka M, Ida T, Yamamoto Y, Okuno Y, Miyamoto S, Nakagawa H, Matsubara K, Chiba T, Muto M. “Novel 5-fluorouracil-resistant human esophageal squamous cell carcinoma cells with dihydropyrimidine dehydrogenase overexpression” Am. J. Cancer Res. 2015;5(8):2431-2440. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4568778/
  • Kimura G, Kadoyama K, Brown J.B, Nakamura T, Miki I, Nishiguchi K, Sakaeda T, Okuno Y. “Antipsychotics-associated serious adverse events in children: An analysis of the FAERS database” Int. J. Med. Sci. 2015;12(2):135-140. Available from: https://doi.org/10.7150/ijms.10453
  • Kawasaki K, Kondoh E, Chigusa Y, Ujita M, Murakami R, Mogami H, Brown J.B, Okuno Y, Konishi I. “Reliable pre-eclampsia pathways based on multiple independent microarray data sets” Molecular Human Reproduction 2015;21(2):217-224. Available from: https://doi.org/10.1093/molehr/gau096

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  奧野恭史 , 先進医療 Navigator 『医療とAI最前線』, 日本医学出版, p58,-60,2022年2月

• 「薬として使えそうな物質をAIが提案」
  奧野恭史 , ニュートン別冊『ゼロからわかる人口知能 完全版 , p56,-57, 2022年2月

• 「薬として使えそうな物質をAIが提案」
  奧野恭史： ニュートン別冊『ゼロからわかる人口知能 増補第2版 , 2022.1

• 「日本人疾患バリアントデータベース MgeND」
  鎌田真由美,芦根怜,峰晴陽平,奧野恭史, 遺伝子医学39号,株式会社メディカルドゥ, Vol12 No1, p65-69,2022年1月1日発行

• 「これまでの創薬の”常識”をくつがえす新薬たち」
  奧野恭史： ニュートン別冊『くすりの科学知識 , 改訂第3版 , 74-76 , 2021.11

• 「AIによるAKI発症予測」
  櫻木実, 内野詠一郎, 佐藤憲明, 奧野恭史, 柳田素子： 腎臓内科 , 科学評論社 , 第14巻第4, 2021.10

• 「協調的2アミノ酸残基同時変異体の相互作用解析による新規抗体のStructure-Based Design」
  千葉峻太朗, 大田雅照： 医学のあゆみ , 278巻6号 , 2021.8

• 「ライフインテリジェンスコンソーシアム(LINC)」
  奥野恭史：医学のあゆみ , 278巻6号 , 637-640, 2021.8

• 「医薬品開発におけるAI活用」
  岩田浩明，奥野恭史：検査と技術, 第49巻, 第7号, 766-769, 2021.7.1

• 「日本人疾患ゲノム情報統合データベースMGeND」
  鎌田真由美，中津井雅彦，奥野恭史：実験医学増刊, Vol. 39, No.7, 90-96, 2021.5.1

• 「AIと創薬」
  奥野恭史：小児外科, Vol.53, No.4, 378-381, 2021.4.25

• 「ビッグデータを用いた創薬・医科学の最前線」
  奥野恭史：學士會会報, March Vol. 974, 2021-II, 104-109, 2021.3

• 「「富岳」で飛躍するコンピュータ創薬」
  奥野恭史：医学のあゆみ　Vol. 276, No.9 , 837-841, 2021.2.27

• 「人工知能の利活用-診断・治療支援を高度化する予測医療」
  奥野恭史：特集Digital Hypertension, Medical Science Digest, Vol 47 (1), 25-28, 2021.

• 「医療における人工知能「AI」」
  植田彰彦,小島諒介,山口建,濱西潤三,万代昌紀,奥野恭史：産婦人科の実際　69(5), 2020．

• 「データ主導型個別化医療と予測医療」
  奥野恭史,小清水宏,鎌田真由美,玉田嘉紀,小島諒介,内野詠一郎：あいみっく 41(1),2020．

• 「臨床データを用いた医療ビッグデータの解析と創薬への応用」
  内野詠一郎,佐藤憲明,小島諒介,玉田嘉紀,柳田素子,奥野恭史：日腎会誌, 62(2), 24-27，2020．

• 「臨床データからの創薬」
  佐藤憲明，内野詠一郎，奥野恭史：月刊細胞，51(7)，24-27，2019．

• 「創薬のためのタンパク質構造」
  大田雅照，池口満徳：月刊細胞　51(7)，20-23，2019．

• 「AIによる逆合成解析に向けて」
  寺山　慧，石田祥一，奥野恭史：月刊細胞　51(7)，12-15，2019

• 「標的分子探索，ドラッグリポジショニング，オミクスメカニズム解明」
  岩田浩明，小島諒介，玉田嘉紀：月刊細胞　51(7)，4-7，2019

• 「総論　第4次産業革命における創薬イノベーション」
  奥野恭史:月刊細胞　51(7)，2-3，2019.

• 「機械学習を用いた医療データ解析」
  佐藤憲明, 小島諒介，奥野恭史:Medical Science Digest 45(5)，23-26，2019.

• 「計算創薬におけるシミュレーション・機械学習・実験の融合に向けて」
  徳久淳師, 寺山　慧, 奥野恭史:分子シミュレーション学会誌「アンサンブル」21(2), 115-125, 2019.

• 「ビッグデータ創薬」
  奥野恭史:「ビッグデータ創薬」最新医学　74(3),62-66,2019.

• 「医療ｘAI－AIの基礎と医療への応用―」
  玉田嘉紀, 佐藤憲明, 奥野恭史: 整形・災害外科　62，215-221，2019.

• 「囲碁AIから逆合成解析へ−情報科学からのアプローチ」
  寺山　慧, 石田祥一, 奥野恭史：化学 74(2), 36-40, 2019.2

• 「特集：AIと創薬ーコンピュータ支援有機合成の現在」
  松原誠二郎, 寺山　慧, 奥野恭史：MEDCHEM NEWS　28(4), 181-186, 2018.

• 「非小細胞肺がん（NSCLC）における薬剤耐性獲得メカニズムの予測」
  荒木望嗣, 奥野恭史：がん分子標的治療　16(3), 53-58, 2018.

• 「疾患レジストリーと知識データベース」
  奥野恭史, 中津井雅彦, 鎌田真由美：日本医師会雑誌 第147巻第7号, 1395-1399, 2018.10.1

• 「がんゲノム医療用知識データベース」
  中津井雅彦, 鎌田真由美, 奥野恭史：実験医学　増刊 36(15), 36-40, 2018.

• 特集「人工知能（ＡＩ）がもたらす創薬イノベーション」
  ７. データベース・計算環境～知識データベース・AI基盤
  長尾知生子, 種石　慶：医薬ジャーナル　54(9), 113-117, 2018.

• 特集「人工知能（ＡＩ）がもたらす創薬イノベーション」
  6.  バイオメディカル・基礎から臨床への開発プロセス（２）
  　3）治験・市販後・メディカルアフェアーズ
  中津井雅彦, 飯塚博美, 新村直哉ほか：医薬ジャーナル　54(9), 109-112, 2018.

• 特集「人工知能（ＡＩ）がもたらす創薬イノベーション」
  6. バイオメディカル・基礎から臨床への開発プロセス（２）
  ２）バイオロジクス・製剤・ロボティクス～医薬品製造・バイオロジクス生産に向けたAI技術開発～
  荒木通啓：医薬ジャーナル　54(9), 105-108, 2018.

• 特集「人工知能（ＡＩ）がもたらす創薬イノベーション」
  3.  診断・治療～臨床・診断
  鎌田真由美：医薬ジャーナル　54(9), 73-77, 2018.

• 特集「人工知能（ＡＩ）がもたらす創薬イノベーション」
  ２．予防・先制医療における疾患発症予測モデルと細菌叢
  内野詠一郎, 佐藤憲明, 奥野恭史：医薬ジャーナル　54(9), 69-72, 2018.

• 特集「人工知能（ＡＩ）がもたらす創薬イノベーション」
  序文  ～LINCの設立とAI創薬～
  奥野恭史, 水口賢司, 本間光貴：医薬ジャーナル　54(9), 65-67, 2018.

• 「ビッグデータ時代のゲノム創薬」
  内野詠一郎、荒木通啓、奥野恭史：分子精神医学 2018.7

• 「次世代創薬シミュレーション」
  荒木望嗣, 奥野恭史：生体の科学, 金原一郎記念医学医療振興財団, 69(4), 310-314, 2018.

• 「最先端研究：ビッグデータ解析と分子シミュレーションを活用したインシリコ創薬」
  千葉峻太朗, 荒木望嗣, 奥野恭史：シミュレーション　37(1), 42-49, 2018.

• 「AIを用いた創薬の実際と今後の展望」
  小島諒介, 奥野恭史：INNERVISION　33(7), 71-73, 2018.

• 「集合知によるAI創薬の展開‐ライフインテリジェンスコンソーシアム（LINC）」
  荒木通啓, 中津井正彦, 奥野恭史：国際医薬品情報 1106, 16-22, 2018.

• 「スーパーコンピュータ・人工知能によるインシリコ創薬」
  岩田浩明, 荒木望嗣, 奥野恭史：化学工業　69(5), 58-66, 2018.

• 「人工知能を用いた創薬（AI創薬）」
  藤原　大, 鎌田真由美, 奥野恭史：癌と化学療法　45(4), 593-596, 2018.

• 「AIによる腎生検組織画像の診断支援システム開発」
  内野詠一郎, 佐藤憲明, 奥野恭史, 柳田素子：腎臓　40(115), 15-18, 2018.

• 「ビッグデータの創薬への応用」
  種石　慶, 徳久淳師, 奥野恭史：EPSホールディングス「遥か」Vol.11, 2018

• 「スーパーコンピュータが可能にする医療と創薬」
  内野詠一郎, 中津井雅彦, 奥野恭史：月刊腎臓内科・泌尿器科 7(2), 160-164, 2018.

• テレメディシン「遠隔医療の現状と課題：遠隔医療の実現とビッグデータ」
  佐藤憲明, 岩田浩明, 奥野恭史：医学のあゆみ, 医歯薬出版　264(7), 2018.

• 人工知能と医療のハーモニー「人工知能を用いたビッグデータ創薬」
  藤原　大, 中津井雅彦, 奥野恭史：医学のあゆみ, 医歯薬出版 263(8), 2018.

• 「臨床ビッグデータ解析の展望―実臨床データとゲノム情報への応用」
  内野詠一郎, 種石　慶, 中津井雅彦, 鎌田真由美, 荒木望嗣, 奥野恭史：生体医工学　55(4):173-182, 2017.

• 「創薬におけるAIの可能性」
  種石　慶, 岩田浩明, 小島諒介, 奥野恭史：日本化学会情報化学部会誌, 35(3), 212, 2017.

• 「Precision Medicine実現に必要なシミュレーション科学とデータ科学（data centric science）」
  佐藤憲明, 内野詠一郎, 鎌田真由美, 奥野恭史：腫瘍内科 20(4), 306-312, 2017.

• 「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」
  中津井雅彦, 種石　慶, 奥野恭史：実験医学 35(1), 2017.

• 「スパコン「京」によるインシリコ創薬」
  中津井雅彦, 鎌田真由美, 荒木望嗣, 奥野恭史：日本薬理学雑誌 149(6), 281-287, 2017.

• 「網羅的癌関連遺伝子変異検査（OncoPrime^TM）による膵癌ゲノム異常解析と治療への応用」
  金井雅史, 高忠之, 松本繁巳, 武藤学, 児玉裕三, 増井俊彦, 高折恭一, 南口早智子, 鎌田真由美, 中津井雅彦, 奥野恭史, 佐久間朋寛, 望月洋明, 広島明宣： 胆と膵 37 9月号　医学図書出版（株）, 2016.

• 「生体分子システムの機能制御による革新的創薬基盤の構築」
  奥野恭史, BioSupercomputing Newsletter 13, 12-13, 2015.

• 「神戸インシリコ創薬拠点の形成について」
  奥野恭史,都市政策,160,20-26,2015

• 「スパコン創薬から観えるインシリコ創薬の未来」
  奥野恭史,薬剤学,74(5),327-334,2014

• “Compound-protein interaction prediction within chemogenomics: theoretical concepts, practical usage, and future directions“
  Brown, J.B., Niijima, S., Okuno, Y.,Molecular Informatics,32,906–921,2013

• 「スーパーコンピュータ「京」が拓くコンピュータ創薬の未来」
  中津井雅彦, 奥野恭史,情報処理,55(8),836-841,2014.

• 「スーパーコンピュータ「京」が拓くコンピュータ創薬の未来」
  奥野恭史,ファルマシア,50(5),433-437,2014.

• 「コンピュータ創薬の基盤となる計算化学分野の受賞」
  奥野恭史,医学のあゆみ,247,2013

• “Data Mining of the Public Version of the FDA Adverse Event Reporting System”
  Sakaeda, T., Tamon, A., Kadoyama, K., Okuno, Y., Int.J.Med.Sci., 10(7), 796-803, 2013

• 「ケモセントリックアプローチ」
  新島　聡, CICSJ Bulletin, 30(4), 61-64, 2012

• 「ケモゲノミクス：ゲノムからケミカルスペースへ」
  奥野恭史, CICSJ Bulletin, 30(4), 60, 2012

• 「医薬品有害事象データベースを用いたデータマイニング」
  五島　誠, 奥野恭史, SAR News, 23, 12-18, 2012

• “Staying grounded in reality – an opinion on why informatics students should have laboratory experience“
  Brown, J.B., Mol. Inf. , 31(11-12):777-781, 2012

• “Systems biology and systems chemistry: new directions for drug discovery“
  Brown, J.B., Okuno, Y., Chem Biol, 19(1), 23-8, 2012

• “Unifying Bioinformatics and Chemoinformatics for Drug Design”
  Brown, J.B., Okuno, Y., Systems and Computational Biology — Bioinformatics and Computational Modeling, InTech, 99-120, 2011

• 「医薬品による有害事象の自発報告システム」
  栄田敏之, 角山香織, 奥野恭史, 人工知能学会誌, 26(2), 126-130, 2011

• 「創薬バリューチェインのインシリコ技術を活用した阻害剤開発の試み」
  井上　豪, 安達宏昭, 森　勇介, 高野和文, 松村浩由, 村上　聡, 福西快文, 中村春木, 木下誉富, 仲西　功, 奥野恭史, 南方聖司, 佐久間俊広, 高田俊和, 北島正人, 福岡良忠, 坂田恒昭, 日本結晶学会誌, 52, 89-94, 2010

• 「化合物-タンパク質活性空間における特徴選択」
  新島　聡, 奥野恭史, Technical Reports of the 12th Workshop on Information-Based Induction Sciences (IBIS2009), 37, 2009

• “Biogenesis and Function Mechanisms of Micro-RNAs and Their Role as Oncogenes and Tumor Suppressors”
  Tsuchiya, S., Terasawa, K., Kunimoto, R., Okuno, Y., Sato, F., Shimizu, K. and Tsujimoto, G., Systems Biology, Springer, 183-189, 2009

• “New Progress in Crystallization Technology of Membrane Protein and Introduction of Pharamaceutical Innovation Value Chain”
  Inoue, T., Adachi, H., Murakami, S., Takano, K., Matsumura, H., Mori, Y., Fukunishi, Y., Nakamura, H., Kinoshita, T., Nakanishi, I., Okuno, Y., Minakata, S., Shimojo, S., Sakata, T., YAKUGAKU ZASSHI, 128(4), 497-505, 2008

• 「ケミカルゲノミクス情報を用いた新規リガンド探索手法」
  新島　聡, 奥野恭史, 日本薬理学雑誌, 133:3, 173-174, 2009

• 「ケミカル・バイオ情報に基づく創薬インフォマティクス研究」
  奥野恭史, Pharma VISION NEWS, 9, 13-16, 2007

Book

• 『プレシジョン・メディシンを加速する「創薬ビッグデータ統合システム」の推進』
  荒木望嗣、鎌田真由美、奥野恭史.「富岳」が拓く計算科学の未来, milsil, 2020年11月1日発行.

• 『第1章第1節「ゲノム解析の概要と現状」』
  鎌田真由美, 奥野恭史. 「医薬品開発におけるオミクス解析技術～ゲノム・トランスクリプトーム・プロテオーム・メタボローム～」, 情報機構, 2020年3月発行.

• 『スーパーコンピュータと創薬』
  奥野恭史. 高校生向け図集「サイエンスビュー　化学総合資料」P296-297, 実教出版. 2019年3月10日発行.

• 『AI導入によるバイオテクノロジーの発展』
  徳久淳師、種石　慶、奥野恭史：「創薬におけるビッグデータの可能性」,シーエムシー出版(2018).

• 『in silico創薬におけるスクリーニングの高速化・高精度化技術』
  徳久淳師、金田亮、岩田浩明、馬彪、井阪悠太、奥野恭史（担当項目にて共著）. 技術情報協会 (2018)

• 『日本人のためのゲノム医療用AI、2018年度中に試行開始』
  鎌田真由美、奥野恭史. 週刊医学界新聞、第3254号（2018.1.1）

• 実験医学別冊  『あなたのラボにAI（人工知能）×ロボットがやってくる―研究に生産性と創造性をもたらすテクノロジー』
  夏目　徹／編．種石　慶，岩田浩明，小島諒介，奥野恭史／著「創薬とAIの良好な関係」（2017）

• 『変貌する医療市場』研究・技術革新・社会実装
  奥野恭史（共著）. 監修：木村廣道.　かんき出版　(2017)

• 『人工知能・機械学習・ディープラーニング関連技術とその活用』
  岩田浩明, 種石慶, 奥野恭史　共著「創薬と人工知能」　(株)情報機構　(2016)

• 『ビッグデータの収集、調査、分析と活用事例』
  奥野恭史　共著　技術情報協会　(2014.11.28)

• 『スーパーコンピュータ「京」によるビッグデータ創薬』
  奥野恭史共著「ビッグデータの収集、調査、分析と活用事例」,(株)技術情報協会　(2014)

• 『生命のビッグデータ利用の最前線』
  奥野恭史共著,シーエムシー出版(2014)

• 『化学便覧 応用化学編 第7版』
  奥野恭史共著, 丸善出版(株)(2014)

• 『疾患克服をめざしたケミカルバイオロジー（実験医学増刊）』
  奥野恭史共著　,㈱羊土社

• 『シミュレーション辞典』
  奥野恭史　共著, ㈱コロナ社(2012)

• 『最新 創薬インフォマティクス活用マニュアル』
  奥野恭史編集, (株)メディカルドゥ(2011)

• 『Handbook of Systems Toxicology』
  奥野恭史　共著, Wiley-Blackwell(2011)

• 『薬学の展望とロードマップ』
  奥野恭史　共著, 日本薬学会(2010)

• 『医薬ジャーナル　増刊号 新薬展望2010』
  奥野恭史　共著, ㈱医薬ジャーナル社(2010)

• 『次世代創薬テクノロジー 実践：インシリコ創薬の最前線』
  奥野恭史　共著, ㈱メディカル・ドゥ(2009)

• 『コンピューターで薬を創ろう』
  奥野恭史　共著, 化学同人(2009)

• 『インシリコ創薬科学 －ゲノム情報から創薬へ－』
  奥野恭史　共著･編集, 京都廣川書店(2008)

Patent

• 特願2021-066780
  「長時間分子動力学シミュレーションによる変異型タンパク質に対する治療薬探索法 」

  発明者：奥野恭史, 荒木望嗣, 片山量平

   

• 特願K20210055
  「未熟児網膜症の信仰予測装置、予測方法 」

  発明者：福嶋葉子、奥野恭史、小島諒介

   

• 特願2021-092251
  「予測装置、予測方法、予測プログラム、予測システム」

  発明者：西真宏, 的場聖明, 奥野恭史, 内野詠一郎

   

• 特願2020-178148
  「健康改善経路探索装及び健康改善経路探索方法」

  発明者：奥野恭史, 小島諒介, 内野詠一郎, 中路重之, 伊東健, 村下公一, 中村和貴

   

• 特願2020-002923
  「特徴ネットワーク抽出装置、コンピュータプログラム、特徴ネットワーク抽出方法及びベイジアンネットワーク分析方法 」

  発明者：奥野恭史, 玉田嘉紀

   

• 特願201780066425.4
  「PD-1シグナル阻害剤による疾患治療における有効性判定マーカー」

  発明者：本庶 佑, 茶本健司, 松田文彦, ファガラサン・シドニア, 奥野恭史

   

• 特願17867434.7
  「PD-1シグナル阻害剤による疾患治療における有効性判定マーカー」

  発明者：本庶 佑, 茶本健司, 松田文彦, ファガラサン・シドニア, 奥野恭史

   

• 特願2007-53322
  「PD-1シグナル阻害剤による疾患治療における有効性判定マーカー」

  発明者：本庶 佑, 茶本健司, 松田文彦, ファガラサン・シドニア, 奥野恭史

   

• 特願2018-549058
  「PD-1シグナル阻害剤による疾患治療における有効性判定マーカー」

  発明者：本庶 佑, 茶本健司,松田文彦, ファガラサン・シドニア, 奥野恭史

   

• 特願106137915
  「PD-1シグナル阻害剤による疾患治療における有効性判定マーカー」

  発明者：本庶 佑, 茶本健司, 松田文彦, ファガラサン・シドニア, 奥野恭史

   

• 特願2014-532989 5946045, 6750177
  「化合物設計装置,化合物設計方法,及びコンピュータプログラム 」

  発明者：奥野恭史, 金井千里, 吉川達也, 多門啓子

   

• 特願2007-53322
  「ケミカルゲノム情報に基づく、タンパク質－化合物相互作用の予測と化合物ライブラリーの合理的設計 」

  発明者：奥野恭史, 種石慶, 辻本豪三

   

• 特願2007-53322
  「マイクロＲＮＡ標的遺伝子予測装置」

  発明者：奥野恭史, 辻本豪三, 国本亮, 寺澤和哉, 土屋創健, 秋山英雄, 妙本明

   

• 特願2016-074085
  「アントラニルアミド誘導体およびそれを含有するTLR3が関与する疾患の治療剤」

  発明者：奥野恭史, 木下　茂, 土黒一郎, 上田真由美