原著論文Original Article

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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
    • Miyaguchi I,Sato M,Kashima A,Nakagawa H,Kokabu Y, M Biao,Matsumoto S,Tokuhisa A, Ohta M& Ikeguchi M, Machine learning to estimate the local quality of protein crystal structures,Scientific Report,11, 23599 , 2021,https://doi.org/10.1038/s41598-021-02948-y
    • 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
    • Arakawa Y, Sasaki K, Mineharu Y, Uto M, Mizowaki T, Mizusawa J, Sekino Y, Ono T, Aoyama H, Satomi K, et al. A randomized phase III study of short-course radiotherapy combined with Temozolomide in elderly patients with newly diagnosed glioblastoma; Japan clinical oncology group study JCOG1910 (AgedGlio-PIII). BMC Cancer. 21:1105., 2021, https://doi.org/10.1186/s12885-021-08834-0
    • 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
    • Terayama K, Sumita M, Katouda M, Tsuda K, Okuno Y, Efficient Search for Energetically Favorable Molecular Conformations against Metastable States via Gray-Box Optimization. Journal of Chemical Theory and Computation, 17:5419–5427, 2021,DOI:https://doi.org/10.1021/acs.jctc.1c00301
    • Matsumoto S, Taniguchi-Tamura H, Araki M, Kawamura T, Miyamoto R, Tsuda C, Shima F, Kumasaka T, Okuno Y*, Kataoka T*, Oncogenic mutations Q61L and Q61H confer active form-like structural features to the inactive state (state 1) conformation of H-Ras protein. Biochemical and Biophysical Research Communications. 565:85-90, 2021,https://doi.org/10.1016/j.bbrc.2021.05.084
    • 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
    • Sato N,Uchino E,Okuno Y. Artificial Intelligence in Kidney Pathology. In: Lidströmer N., Ashrafian H. (eds) Artificial Intelligence in Medicine. Springer, Cham.pp 1-11,2021, https://doi.org/10.1007/978-3-030-58080-3_181-1
    • 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
    • Yamawaki R*, Nankaku M, Umaba C, Ueda M, Liang N, Mineharu Y, Yamao Y, Ikeguchi R, Matsuda S, Miyamoto S, et al. Assessment of neurocognitive function in association with WHO grades in gliomas. Clin Neurol Neurosurg ,208:106824. ,2021,https://doi.org/10.1016/j.clineuro.2021.106824
    • 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
    • Kamada M, Okuno Y*, AIM in genomic basis of medicine: applications. Artificial Intelligence in Medicine. 1-10, 2021 , https://doi.org/10.1007/978-3-030-58080-3_145-1
    • 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
  • 2020)
    • 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