Motoki Shiga
Motoki Shiga
Professor, Tohoku University, Japan
Verified email at
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Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
MP Menden, D Wang, MJ Mason, B Szalai, KC Bulusu, Y Guan, T Yu, ...
Nature communications 10 (1), 2674, 2019
Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open …
J Guinney, T Wang, TD Laajala, KK Winner, JC Bare, EC Neto, SA Khan, ...
The Lancet Oncology 18 (1), 132-142, 2017
A spectral clustering approach to optimally combining numericalvectors with a modular network
M Shiga, I Takigawa, H Mamitsuka
Proceedings of the 13th ACM SIGKDD international conference on Knowledge …, 2007
Sparse modeling of EELS and EDX spectral imaging data by nonnegative matrix factorization
M Shiga, K Tatsumi, S Muto, K Tsuda, Y Yamamoto, T Mori, T Tanji
Ultramicroscopy 170, 43-59, 2016
Multi-fidelity Bayesian optimization with max-value entropy search and its parallelization
S Takeno, H Fukuoka, Y Tsukada, T Koyama, M Shiga, I Takeuchi, ...
International Conference on Machine Learning, 9334-9345, 2020
Structure and properties of densified silica glass: characterizing the order within disorder
Y Onodera, S Kohara, PS Salmon, A Hirata, N Nishiyama, S Kitani, ...
NPG Asia Materials 12 (1), 85, 2020
Machine-learning-based selective sampling procedure for identifying the low-energy region in a potential energy surface: A case study on proton conduction in oxides
K Toyoura, D Hirano, A Seko, M Shiga, A Kuwabara, M Karasuyama, ...
Physical Review B 93 (5), 054112, 2016
Understanding diffraction patterns of glassy, liquid and amorphous materials via persistent homology analyses
Y Onodera, S Kohara, S Tahara, A Masuno, H Inoue, M Shiga, A Hirata, ...
Journal of the Ceramic Society of Japan 127 (12), 853-863, 2019
Mining significant tree patterns in carbohydrate sugar chains
K Hashimoto, I Takigawa, M Shiga, M Kanehisa, H Mamitsuka
Bioinformatics 24 (16), i167-i173, 2008
Application of machine learning techniques to electron microscopic/spectroscopic image data analysis
S Muto, M Shiga
Microscopy 69 (2), 110-122, 2020
Annotating gene function by combining expression data with a modular gene network
M Shiga, I Takigawa, H Mamitsuka
Bioinformatics 23 (13), i468-i478, 2007
Exploring a potential energy surface by machine learning for characterizing atomic transport
K Kanamori, K Toyoura, J Honda, K Hattori, A Seko, M Karasuyama, ...
Physical Review B 97 (12), 125124, 2018
Accelerated discovery of proton-conducting perovskite oxide by capturing physicochemical fundamentals of hydration
J Hyodo, K Tsujikawa, M Shiga, Y Okuyama, Y Yamazaki
ACS Energy Letters 6 (8), 2985-2992, 2021
Genome-wide integration on transcription factors, histone acetylation and gene expression reveals genes co-regulated by histone modification patterns
Y Natsume-Kitatani, M Shiga, H Mamitsuka
PloS one 6 (7), e22281, 2011
Efficiently finding genome-wide three-way gene interactions from transcript-and genotype-data
M Kayano, I Takigawa, M Shiga, K Tsuda, H Mamitsuka
Bioinformatics 25 (21), 2735-2743, 2009
A variational bayesian framework for clustering with multiple graphs
M Shiga, H Mamitsuka
IEEE Transactions on Knowledge and Data Engineering 24 (4), 577-590, 2010
Efficient semi-supervised learning on locally informative multiple graphs
M Shiga, H Mamitsuka
Pattern Recognition 45 (3), 1035-1049, 2012
Detecting Differentially Coexpressed Genes from Labeled Expression Data: A Brief Review
M Kayano, M Shiga, H Mamitsuka
IEEE/ACM Transactions on Computational Biology and Bioinformatics 11 (1 …, 2014
ROS-DET: robust detector of switching mechanisms in gene expression
M Kayano, I Takigawa, M Shiga, K Tsuda, H Mamitsuka
Nucleic acids research 39 (11), e74-e74, 2011
Upper bound for variational free energy of Bayesian networks
K Watanabe, M Shiga, S Watanabe
Machine Learning 75, 199-215, 2009
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