原著論文Original Article

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    • 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
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    • 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
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    • 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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    • Okamoto M, Kojima R, Ueda A, Suzuki M, Okuno Y , Characterizing eye-gaze positions of people with severe motor dysfunction: Novel scoring metrics using eye-tracking and video analysis , PLOS ONE, 17(8):e0265623, 2022/8/31, doi:10.1371/journal.pone.0265623
    • Iwata H*, Matsuo T, Mamada H, Motomura T, Matsushita M, Fujiwara T, Maeda K, Handa K*, Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data, Journal of chemical information and modeling ,  62(17):4057-4065, 2022/8/22, doi: 10.1021/acs.jcim.2c00318
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    • 松本 篤幸, 寺山 慧, 奥野 恭史, 深層学習技術を用いたクライオ電子顕微鏡データに潜むタンパク質運動性情報の抽出, 生物物理, 62(3),193-197, 2022/7/25, https://doi.org/10.2142/biophys.62.193
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    • Nishiwaki S, Shizuta S, Tanaka M, Komasa A, Kohjitani H, Kimura T, A case of preexcitation syndrome showing atypical atrioventricular nodal reentrant tachycardia and orthodromic atrioventricular reciprocating tachycardia with a bystander concealed nodoventricular/ nodofascicular pathway, Heart Rhythm Case Reports, 8(7):529-534, 2022/7, doi:10.1016/j.hrcr.2022.05.003
    • Matsui Y, Mineharu Y, Noguchi Y, Hattori EY, Kubota H, Hirata M, Miyamoto S, Sugiyama H, Arakawa Y, Kamikubo Y, Chlorambucil-conjugated PI-polyamides (Chb-M’), a transcription inhibitor of RUNX family, has an anti-tumor activity against SHH-type medulloblastoma with p53 mutation. , Biochem. Biophys. Res. Commun, 620:150-157, 2022/6/28, doi:10.1016/j.bbrc.2022.06.090
    • Sato K*, Sasaki R, Matsuda R, Nakagawa M, Ekimoto T, Yamane T, Ikeguchi M, Tabata K, Noji H, and Kinbara K*, Supramolecular Mechanosensitive Potassium Channel Formed by Fluorinated Amphiphilic Cyclophane, Journal of the American Chemical Society, 144(26):11802-11809, 2022/6/21,  doi:10.1021/jacs.2c04118
    • Hasegawa T, Arakawa Y, Minamiguchi S, Mineharu Y, Nakajima S, Nakajima K, Hirose T, Haga H, Miyamoto S. , Cerebral Tufted Angioma with Gradually Developing Peritumoral Edema: A Case Report , NMC Case Report Journal ,, 9 , 187–192 , 2022/6/21, https://doi.org/10.2176/jns-nmc.2022-0079
    • Natsume A, Arakawa Y, Narita Y, Sugiyama K, Hata N, Muragaki Y, Shinojima N, Kumabe T, Saito R, Motomura K, Mineharu Y, Miyakita Y, Yamasaki F, Matsushita Y, Ichimura K, Ito K, Tachibana M, Kakurai Y, Okamoto N, Asahi T, Nishijima S, Yamaguchi T, Tsubouchi H, Nakamura H, Nishikawa R. , The first-in-human phase I study of a brain penetrant mutant IDH1 inhibitor DS-1001 in patients with recurrent or progressive IDH1-mutant gliomas. Neuro-Oncology , 25(2):326-336, 2022/6/20, doi:10.1093/neuonc/noac155 
    • Sato N, Tamada Y, Yu G, Okuno Y, CBNplot Bayesian network plots for enrichment analysis, Bioinformatics, 38(10):2959-2960, 2022/5/13, doi:10.1093/bioinformatics/btac175
    • Tanji M, Mineharu Y, Sakata A, Okuchi S, Fushimi Y, Oishi M, Terada Y, Sano N, Yamao Y, Arakawa Y, Yoshida K, Miyamoto S, High intratumoral susceptibility signal grade on susceptibility-weighted imaging: a risk factor for hemorrhage after stereotactic biopsy, Journal of Neurosurgery, 138(1):120-127, 2022/5/13, doi:10.3171/2022.4.JNS212505 
    • 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
    • Seto K, Shimizu J, Masago K, Araki M, Katayama R, Sagae Y, Fujita S, Horio Y, Sasaki E, Kuroda H, Okubo K, Okuno Y, Hida T, Sensitivity to dabrafenib and trametinib treatments in patients with non-small-cell cancer harboring BRAF compound mutations: A pooled analysis of BRAF p.V600E-positive advanced non-small-cell lung cancer, Cancer Genetics, 266-267:1-6, 2022/5/11 , doi:10.1016/j.cancergen.2022.05.001
    • 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 
    • Kanada R, Terayama K, Tokuhisa A, Matsumoto S, Okuno Y , Enhanced Conformational Sampling with an Adaptive Coarse-Grained Elastic Network Model Using Short-Time All-Atom Molecular Dynamics . Journal of chemical theory and computation. , 18 (4) , 2062-2074 , 2022/4/18 , https://doi.org/10.1021/acs.jctc.1c01074 
    • 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
    • Kawata M, Fukui A, Mineharu Y, Kikuchi T, Yamao Y, Yamamoto Hattori E, Shiraki A, Mizota T, Furukawa K, Miyamoto S, et al. , A Nationwide Questionnaire Survey on Awake Craniotomy in Japan . Neurologia medico-chirurgica , 2022/3/29, https://doi.org/10.2176/jns-nmc.2021-0290
    • Ishida S , Terayama K , Kojima R, Takasu K, Okuno Y , AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge , Journal of Chemical Information and Modeling , Vol.62, No.6 , pp.1357–13672022 , 2022/3/8 , https://doi.org/10.1021/acs.jcim.1c01074
    • Huang K, Matsumura H, Zhao Y, Herbig M, Yuan D, Mineharu Y, Harmon J, Findinier J, Yamagishi M, Ohnuki S, Nitta N, Grossman AR, Ohya Y, Mikami H, Isozaki A, Goda K , Deep imaging flow cytometry . Lab on a Chip , 22 , 876-889 , 2022/2/4, https://doi.org/10.1039/D1LC01043C
    • 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
    • Harada Y,Sato A,Araki M,Matsumoto S,Isaka Y,Sagae Y,Abe T,Aoyagi Y,Sueoka E,Okuno Y,Kimura S & Sueoka-Aragane N, Integrated approach to functional analysis of an ERBB2 variant of unknown significance detected by a cancer gene panel test, Cellular Oncology, 2022, https://doi.org/10.1007/s13402-021-00656-3
    • Umaba C,Mineharu Y,Liang N,Mizota T,Yamawaki R,Ueda M,Yamao Y,Nankaku M,Miyamoto S,Matsuda S,Inadomi H,Arakawa Y,Intraoperative hand strength as an indicator of consciousness during awake craniotomy: a prospective, observational study,Scientific Reports,12(1):216., 2022, https://www.nature.com/articles/s41598-021-04026-9
  • 2021)
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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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    • Mai Adachi Nakazawa, Yoshinori Tamada, Yoshihisa Tanaka, Marie Ikeguchi, Kako Higashihara & Yasushi Okuno,Novel cancer subtyping method based on patient-specific gene regulatory network,Scientific Reports volume 11, Article number: 23653, 2021, DOI:https://doi.org/10.1038/s41598-021-02394-w
    • Nakamura H, Takami H, Yanagisawa T, Kumabe T, Fujimaki T, Arakawa Y, Karasawa K, Terashima K, Yokoo H, Fukuoka K, Sonoda Y, Sakurada K, Mineharu Y, Soejima T, Fujii M, Shinojima N, Hara J, Yamasaki K, Fujimura J, Takahashi M, Suzuki T, Sato I, Nishikawa R, Sugiyama K, guideline committee in The Japan Society for Neuro-Oncology (JSNO) Task Force on Central Nervous System Germ Cell Tumors. The Japan Society for Neuro-Oncology Guideline on the Diagnosis and Treatment of Central Nervous System Germ Cell Tumors. Neuro. Oncol. noab242 , 2021, https://doi.org/10.1093/neuonc/noab242
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    • Makino Y, Arakawa Y, Yoshioka E, Shofuda T, Minamiguchi S, Kawauchi T, Tanji M, Kanematsu D, Nonaka M, Okita Y, Kodama Y, Mano M, Hirose T, Mineharu Y, Miyamoto S, Kanemura Y. Infrequent RAS mutation is not associated with specific histological phenotype in gliomas. BMC Cancer. ,21:1025. , 2021,https://doi.org/10.1186/s12885-021-08733-4
    • Mineharu Y, Takagi Y, Koizumi A, Morimoto T, Funaki T, Hishikawa T, Araki Y, Hasegawa H, Takahashi JC, Kuroda S, Houkin K, and Miyamoto S: on behalf of the SUPRA Japan Study Group. Genetic and non-genetic factors for contralateral progression of unilateral moyamoya disease: the first report from the SUPRA Japan Study Group, J Neurosurg, 2021,https://doi.org/10.3171/2021.3.JNS203913
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    • Iwata H, Kojima R, Okuno Y*, AIM in Pharmacology and Drug Discovery. Artificial Intelligence in Medicine. 1-9, 2021,https://doi.org/10.1007/978-3-030-58080-3_145-1
    • Uchino E,Sato N, Okuno Y, Artificial Intelligence in Predicting Kidney Function and Acute Kidney Injury, Artificial Intelligence in Medicine,pp 1-17,2021/8,https://doi.org/10.1007/978-3-030-58080-3_270-1
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    • Tsutsui T, Arakawa Y*, Makino Y, Kataoka H, Mineharu Y, Minamiguchi S, Hirose T, Nobusawa S, Nakano Y, Ichimura K, Haga H, Miyamoto S. Spinal cord astroblastoma with EWSR1-BEND2 fusion classified as HGNET-MN1 by methylation classification: a case report　Brain Tumor Pathology volume 38, pages283–289 ,2021,https://doi.org/10.1007/s10014-021-00412-3
    • Mineharu Y, Miyamoto S. RNF213 and GUCY1A3 in Moyamoya Disease: Key Regulators of Metabolism, Inflammation, and Vascular Stability. Front Neurol ,12:1–12. ,2021,https://doi.org/10.3389/fneur.2021.687088
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    • Kamada M, Takagi A, KojimaR, TanakaY, Nakatsui M, Tanabe N, Hirata M, Yoshida T, Okuno Y , Network-based pathogenicity prediction for variants of uncertain significance. bioRxiv, 2021 ,https://doi.org/10.1016/j.bbrc.2021.05.084
    • Makino Y, Arakawa Y, Yoshioka E, Shofuda T, Kawauchi T, Terada Y, Tanji M, Kanematsu D, Mineharu Y, Miyamoto S, et al. Prognostic stratification for IDH wild type lower grade astrocytoma by Sanger sequencing and copy number alteration analysis with MLPA. Sci Rep volume 11, Article number: 14408,1–12., 2021 , https://doi.org/10.1038/s41598-021-93937-8
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    • Ma B, Terayama K*, Matsumoto S, Isaka Y, Sasakura Y, Iwata H, Araki M, Okuno Y*, Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations. Journal of Chemical Information and Modeling. 61(7): 3304–3313, 2021,doi.org/10.1021/acs.jcim.1c00679
    • Shinno K, Arakawa Y, Minamiguchi S, Terada Y, Tanji M, Mineharu Y, Kikuchi T, Haga H, Miyamoto S. Papillary glioneuronal tumor growing slowly for 26 years: illustrative case. J Neurosurg Case Lessons  2:5–8. https://doi.org/10.3171/CASE21266
    • Sato N, Uchino E, Kojima R, Hiragi S, Yanagita M, Okuno Y*, Prediction and visualization of acute kidney injury in intensive care unit using one-dimensional convolutional neural networks based on routinely collected data. Computer Methods and Programs in Biomedicine. 206:106129, 2021,https://doi.org/10.1016/j.cmpb.2021.106129
    • Chiba S, Lim KRQ, Sheri N, Anwar S, Erkut E, Shah MH, Aslesh T, Woo S, Sheikh O, Maruyama R, Takano H, Kunitake K, Duddy W, Okuno Y*, Aoki Y*, Yokota T*, eSkip-Finder: a machine learning-based web application and database to identify the optimal sequences of antisense oligonucleotides for exon skipping. Nucleic Acids Research. gkab442, 2021,https://doi.org/10.1093/nar/gkab442
    • 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
    • Tanaka Y, Higashihara K, Nakazawa M.A, Yamashita F, Tamada Y & Okuno Y. “Dynamic changes in gene-to-gene regulatory networks in response to SARS-CoV-2 infection” Sci Rep. ,11:11241. , 2021,https://doi.org/10.1038/s41598-021-90556-1
    • Nojima S, Terayama K, Shimoura S, Hijiki S, Nonomura N, Morii E, Okuno Y , Fujita K* A deep learning system to diagnose the malignant potential of urothelial carcinoma cells in cytology specimens. Cancer Cytopathology ,6(9), 2445－2454 , 2021/9 , DOI: 10.1002/cncy.22443
    • Nakamura K, Kojima R, Uchino E, Ono K, Yanagita M, Murashita K, Itoh K, Nakaji S & Okuno Y. “Health improvement framework for actionable treatment planning using a surrogate Bayesian model” Nat Commun. ,12:3088. , 2021, https://doi.org/10.1038/s41467-021-23319-1
    • Araki M, Matsumoto S, Bekker GJ, Isaka Y, Sagae Y, Kamiya N & Okuno Y. “Exploring ligand binding pathways on proteins using hypersound-accelerated molecular dynamics” Nat Commun. .12:2793. , 2021,https://doi.org/10.1038/s41467-021-23157-1
    • Matsumoto S, Ishida S, Araki M, Kato T, Terayama K, Okuno Y. “Extraction of protein dynamics information from cryo-EM maps using deep learning” Nat Mach Intell. ,3:153-160. , 2021,https://doi.org/10.1038/s42256-020-00290-y
    • Iwata H, Matsuo T, Mamada H, Motomura T, Matsushita M, Fujiwara T, Maeda K, Handa K. “Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning” Journal of Pharmaceutical Sciences. , 2021,https://doi.org/10.1016/j.xphs.2021.01.020
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    • Kawaguchi C, Shintani N, Hayata-Takano A, Hatanaka M, Kuromi A, Nakamura R, Yamano Y, Shintani Y, Nagai K, Tsuchiya S, Sugimoto Y, Ichikawa A, Okuno Y, Urade Y, Hirai H, Nagata KY, Nakamura M, Narumiya S, Nakazawa T, Kasai A, Ago Y, Takuma K, Baba A, Hashimoto H. “Lipocalin-type prostaglandin D synthase regulates light-induced phase advance of the central circadian rhythm in mice” Commun Biol. 2020;3(1):557. Available from: http://doi.org/10.1038/s42003-020-01281-w
    • Hagihara H, Ienaga N, Enomoto D, Takahata S, Ishihara H, Noda H, Tsuda K, Terayama K. “Computer Vision–Based Approach for Quantifying Occupational Therapists’ Qualitative Evaluations of Postural Control” Occupational Therapy International. 2020:8542191. Available from: http://doi.org/10.1155/2020/8542191
    • 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
    • Hiroaki Iwata, Naoto Kanda, Mitsugu Araki, Yukari Sagae, Katsuyoshi Masuda, Yasushi Okuno. “Discovery of Natural TRPA1 Activators through Pharmacophore-based Virtual Screening and a Biological Assay” Bioorganic & Medicinal Chemistry Letters. 2020:127639. Available from: https://doi.org/10.1016/j.bmcl.2020.127639
    • Mitsugu Araki, Naoto Kanda, Hiroaki Iwata, Yukari Sagae, Katsuyoshi Masuda, Yasushi Okuno. “Identification of a New Class of Non-Electrophilic TRPA1 Agonists by a Structure-Based Virtual Screening Approach. Bioorganic & Medicinal Chemistry Letters. 2020;30(11):127142. Available from: https://doi.org/10.1016/j.bmcl.2020.127142
    • Gert-Jan Bekker, Mitsugu Araki, Kanji Oshima, Yasushi Okuno, Narutoshi Kamiya. “Exhaustive Search of the Configurational Space of Heat-Shock Protein 90 With Its Inhibitor by Multicanonical Molecular Dynamics Based Dynamic Docking” Journal of Computational Chemistry. 2020;41(17):1606-1615. Available from: https://doi.org/10.1002/jcc.26203
    • Ryosuke Shibukawa, Shoichi Ishida, Kazuki Yoshizoe, Kunihiro Wasa, Kiyosei Takasu, Yasushi Okuno, Kei Terayama, Koji Tsuda. “CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration” Journal of Cheminformatics. 2020;12:52. Available from: https://doi.org/10.1186/s13321-020-00452-5
    • Uchino E, Suzuki K, Sato N, Kojima R, Tamada Y, Hiragi S, Yokoi H, Yugami N, Minamiguchi S, Haga H, Yanagita M, Okuno Y. “Classification of glomerular pathological findings using deep learning and nephrologist–AI collective intelligence approach” Int. J. Med. Inform. 2020;141:104231. Available from: https://doi.org/10.1016/j.ijmedinf.2020.104231
    • Matsumoto S, Araki M, Isaka Y, Ono F, Hirohashi K, Ohashi S, Muto M, Okuno Y. “E487K-Induced Disorder in Functionally Relevant Dynamics of Mitochondrial Aldehyde Dehydrogenase 2” Biophys. J. 2020 Jul 10; Available from: https://doi.org/10.1016/j.bpj.2020.07.002
    • Araki M, Kanegawa N, Iwata H, Sagae Y, Ito K, Masuda K, Okuno Y. “Hydrophobic interactions at subsite S1′ of human dipeptidyl peptidase IV contribute significantly to the inhibitory effect of tripeptides” Heliyon. 2020;6(6):e04227. Available from: https://doi.org/10.1016/j.heliyon.2020.e04227
    • Kato K, Masuda T, Watanabe C, Miyagawa N, Mizuochi H, Nagase S, Kamisaka K, Oshima K, Ono S, Ueda H, Tokuhisa A, Kanada R, Ohta M, Ikeguchi M, Okuno Y, Fukuzawa K, Honma T. ” High-Precision Atomic Charge Prediction for Protein Systems Using Fragment Molecular Orbital Calculation and Machine Learning” J. Chem. Inf. Model. 2020. Available from: https://doi.org/10.1021/acs.jcim.0c00273
    • Tokuhisa A, Kanada R, Chiba S, Terayama K, Isaka Y, Ma B, Kamiya N, Okuno Y. “Coarse-Grained Diffraction Template Matching Model to Retrieve Multiconformational Models for Biomolecule Structures from Noisy Diffraction Patterns” J. Chem. Inf. Model. 2020;60(6):2803–2818. Available from: https://doi.org/10.1021/acs.jcim.0c00131
    • Yamanaka M, Iwata H, Masuda K, Araki M, Okuno Y, Okamura M, Koiwa J, Tanaka T. “A novel orexin antagonist from a natural plant was discovered using zebrafish behavioural analysis” Eur Rev Med Pharmacol Sci. 2020;24(9):5127-5139. Available from: https://doi.org/10.26355/eurrev_202005_21207
    • Kojima R, Ishida S, Ohta M, Iwata H, Honma T, Okuno Y. “kGCN: a graph-based deep learning framework for chemical structures” J Cheminform [Internet]. 2020 May 12;12(1):32. Available from: https://doi.org/10.1186/s13321-020-00435-6
    • 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
    • Araki M, Kanda M, Iwata H, Sagae Y, Masuda K, Okuno Y. “Identification of a new class of non-electrophilic TRPA1 agonists by a structure-based virtual screening approach” Bioorg Med Chem Lett. 2020;30(11):127142. Available from: https://doi.org/10.1016/j.bmcl.2020.127142
    • Sato N, Kakuta M, Uchino E, Hasegawa T, Kojima R, Kobayashi W, Sawada K, Tamura Y, Tokuda I, Imoto S, Nakaji S, Murashita K, Yanagita M,Okuno Y. “The relationship between cigarette smoking and the tongue microbiome in an East Asian population” J. Oral Microbiol [Internet]. 2020;12(1):1–9. Available from: https://doi.org/10.1080/20002297.2020.1742527
    • Sato N, Kakuta M, Hasegawa T, Yamaguchi R, Uchino E, Kobayashi W, Sawada K, Tamura Y, Tokuda I, Murashita K, Nakaji S, Imoto S, Yanagita M, Okuno Y. “Metagenomic analysis of bacterial species in tongue microbiome of current and never smokers” npj Biofilms Microbiomes [Internet]. 2020;6(1):1–9. Available from: http://dx.doi.org/10.1038/s41522-020-0121-6
    • Ono F, Chiba S, Isaka Y, Matsumoto S, Ma B, Katayama R, Araki M, Okuno Y. “Improvement in predicting drug sensitivity changes associated with protein mutations using a molecular dynamics based alchemical mutation method” Sci Rep [Internet]. 2020;10(1):2161. Available from: https://doi.org/10.1038/s41598-020-58877-9
    • Tanaka Y, Tamada Y, Ikeguchi M, Yamashita F, Okuno Y. “System-Based Differential Gene Network Analysis for Characterizing a Sample-Specific Subnetwork”  Biomolecules. 2020;10(2):306. Available from:https://doi.org/10.3390/biom10020306
    • 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
    • Kawai R, Chiba S, Okuwaki K, Kanada R, Doi H, Ono M, Mochizuki Y, Okuno Y. “Stabilization Mechanism for a Nonfibrillar Amyloid β Oligomer Based on Formation of a Hydrophobic Core Determined by Dissipative Particle Dynamics” ACS Chem Neurosci. 2020;11:385–394. Available from: https://doi.org/10.1021/acschemneuro.9b00602
  • 2019)
    • Koshimizu H, Kojima R, Kario K, Okuno Y. “Prediction of blood pressure variability using deep neural networks” Int J Med Inform [Internet]. 2020;136(October 2019):104067. Available from: https://doi.org/10.1016/j.ijmedinf.2019.104067
    • 小島 諒介, 鈴木真知子, 荒木綾乃, 奥野恭史.「重度障害児（者）のコミュニケーション向上に向けたアイトラッカーを用いた視線分析」日本重症心身障害学会誌, Dec, 2019;44(3):615-623.
    • Ishida S, Terayama K, Kojima R, Takasu K, Okuno Y. “Prediction and Interpretable Visualization of Retrosynthetic Reactions Using Graph Convolutional Networks” J Chem Inf Model 2019;59(12):5026-33. Available from: https://doi.org/10.1021/acs.jcim.9b00538
    • 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
    • Kamada, M., Naktsui,M., Kojima,R., Nohara.S., Uchino,E., Tanishima,S., Sugiyama,M., Kosaki,K., Tokunaga,K., Mizokam,M., Okuno,Y.,”MGeND: an integrated database for Japanese clinical and genomic information” Hum Genome Var 2019;6:53. Available from: https://dx.doi.org/10.1038%2Fs41439-019-0084-4
    • Negami T, Araki M, Okuno Y, Terada T. Calculation of absolute binding free energies between the hERG channel and structurally diverse drugs. Sci Rep [Internet]  2019;9(1):16586. Available from: http://dx.doi.org/10.1038/s41598-019-53120-6
    • Iwata H, Kojima R, Okuno Y. “An in Silico Approach for Integrating Phenotypic and Target-based Approaches in Drug Discovery” Mol Inform. 2020;39(1-2):1900096. Available from: https://doi.org/10.1002/minf.201900096
    • Tokuhisa A. “Characterization of X-ray diffraction intensity function from a biological molecule for single particle imaging” Biophys Physicobiol. 2019;16:430-43. Available from: https://doi.org/10.2142/biophysico.16.0_430
    • Chiba S, Okuno Y, Honma T, Ikeguchi M. “Force-field parametrization based on radial and energy distribution functions” J Comput Chem 2019;40(29):2577–85. Available from: https://doi.org/10.1002/jcc.26035
    • Quy,PN., Kanai, M., Fukuyama, K., Kou, T., Kondo, T., Yamamoto, Y., Matsubara, J., Hiroshima, A., Mochizuki, H., Sakuma, T., Kamada, M., Nakatsui, M., Eso, Y., Seno, H., Masui, T., Masui, K., Minamiguchi, S., Matsumoto, S., Muto, M.”Association between preanalytical factors and tumor mutational burden estimated by next-generation sequencing-based multiplex gene panel assay” The Oncologist. 2019;24(12):e1401-e1408. Available from: https://doi.org/10.1634/theoncologist.2018-0587
    • Ikemura, S., Yasuda, H., Matsumoto, S., Kamada, M., Hamamoto, J., Masuzawa, K., Kobayashi, K., Manabe, T., Arai, D., Nakachi, I., Kawada, I., Ishioka, K., Nakamura, M., Namkoong, H., Naoki, K., Ono, F., Araki, M., Kanada, R., Ma, B., Hayashi, Y., Mimaki, S., Yoh, K., Kobayashi, S., Kohno, T., Okuno, Y., Goto, K., Tsuchihara, K., and Soejima, K. “Molecular dynamics simulation-guided drug sensitivity prediction for lung cancer with rare EGFR mutations” PNAS  2019;116(20):10025-10030. Available from: https://doi.org/10.1073/pnas.1819430116
    • 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
    • Shiraishi, A., Okuda, T., Miyasaka, N., Osugi, T., Okuno, Y., Inoue, J., Satake, H. “Repertoires of G protein-coupled receptors for Ciona-specific neuropeptides” PNAS. b2019;116(16):7847-7856. Available from: https://doi.org/10.1073/pnas.1816640116
    • 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
    • Araki M, Okuno Y. “Molecular Mechanism of Resistance to Kinase Inhibitors Clarified by a Binding Free Energy Computation Method and Its Improvement by Incorporating Protein Flexibility” BIOPHYSICAL JOURNAL. 2018;114(3):56A. Available from: https://doi.org/10.1016/j.bpj.2017.11.361
    • 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