| 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 | 275 | 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 | 161 | 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 | 130 | 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 | 120 | 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 | 109 | 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 | 70 | 2016 |
| 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 | 69 | 2020 |
| 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 | 59 | 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 | 58 | 2008 |
| Application of machine learning techniques to electron microscopic/spectroscopic image data analysis S Muto, M Shiga Microscopy 69 (2), 110-122, 2020 | 42 | 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 | 41 | 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 | 33 | 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 | 28 | 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 | 25 | 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 | 24 | 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 | 23 | 2010 |
| 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 | 21 | 2014 |
| Efficient semi-supervised learning on locally informative multiple graphs M Shiga, H Mamitsuka Pattern Recognition 45 (3), 1035-1049, 2012 | 21 | 2012 |
| 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 | 19 | 2011 |
| Upper bound for variational free energy of Bayesian networks K Watanabe, M Shiga, S Watanabe Machine Learning 75, 199-215, 2009 | 18 | 2009 |
Ichigaku TakigawaKyoto University / Hokkaido UniversityDirección de correo verificada de kyoto-u.ac.jp
