| Similarity-based machine learning methods for predicting drug–target interactions: a brief review H Ding, I Takigawa, H Mamitsuka, S Zhu Briefings in bioinformatics 15 (5), 734-747, 2014 | 465 | 2014 |
| Machine learning for catalysis informatics: recent applications and prospects T Toyao, Z Maeno, S Takakusagi, T Kamachi, I Takigawa, K Shimizu Acs Catalysis 10 (3), 2260-2297, 2019 | 448 | 2019 |
| Toward effective utilization of methane: machine learning prediction of adsorption energies on metal alloys T Toyao, K Suzuki, S Kikuchi, S Takakusagi, K Shimizu, I Takigawa The Journal of Physical Chemistry C 122 (15), 8315-8326, 2018 | 179 | 2018 |
| Machine-learning prediction of the d-band center for metals and bimetals I Takigawa, K Shimizu, K Tsuda, S Takakusagi RSC advances 6 (58), 52587-52595, 2016 | 162 | 2016 |
| Density functional theory calculations of oxygen vacancy formation and subsequent molecular adsorption on oxide surfaces Y Hinuma, T Toyao, T Kamachi, Z Maeno, S Takakusagi, S Furukawa, ... The Journal of Physical Chemistry C 122 (51), 29435-29444, 2018 | 155 | 2018 |
| 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 | 132 | 2007 |
| Obesity suppresses cell-competition-mediated apical elimination of RasV12-transformed cells from epithelial tissues A Sasaki, T Nagatake, R Egami, G Gu, I Takigawa, W Ikeda, T Nakatani, ... Cell reports 23 (4), 974-982, 2018 | 122 | 2018 |
| Machine learning reveals orbital interaction in materials TL Pham, H Kino, K Terakura, T Miyake, K Tsuda, I Takigawa, HC Dam Science and technology of advanced materials 18 (1), 756, 2017 | 111 | 2017 |
| Performance analysis of minimum/spl lscr//sub 1/-norm solutions for underdetermined source separation I Takigawa, M Kudo, J Toyama IEEE transactions on signal processing 52 (3), 582-591, 2004 | 109 | 2004 |
| Graph mining: procedure, application to drug discovery and recent advances I Takigawa, H Mamitsuka Drug discovery today 18 (1-2), 50-57, 2013 | 85 | 2013 |
| Statistical analysis and discovery of heterogeneous catalysts based on machine learning from diverse published data K Suzuki, T Toyao, Z Maeno, S Takakusagi, K Shimizu, I Takigawa ChemCatChem 11 (18), 4537-4547, 2019 | 78 | 2019 |
| CaMPDB: a resource for calpain and modulatory proteolysis D duVERLE, I TAKIGAWA, Y ONO, H SORIMACHI, H MAMITSUKA Genome Informatics 2009: Genome Informatics Series Vol. 22, 202-213, 2010 | 61 | 2010 |
| Mining significant tree patterns in carbohydrate sugar chains K Hashimoto, I Takigawa, M Shiga, M Kanehisa, H Mamitsuka Bioinformatics 24 (16), i167-i173, 2008 | 59 | 2008 |
| Predictions of cleavability of calpain proteolysis by quantitative structure-activity relationship analysis using newly determined cleavage sites and catalytic efficiencies of … F Shinkai-Ouchi, S Koyama, Y Ono, S Hata, K Ojima, M Shindo, M Ueno, ... Molecular & Cellular Proteomics 15 (4), 1262-1280, 2016 | 54 | 2016 |
| MED26 regulates the transcription of snRNA genes through the recruitment of little elongation complex H Takahashi, I Takigawa, M Watanabe, D Anwar, M Shibata, ... Nature communications 6 (1), 5941, 2015 | 54 | 2015 |
| Field independent probabilistic model for clustering multi-field documents S Zhu, I Takigawa, J Zeng, H Mamitsuka Information Processing & Management 45 (5), 555-570, 2009 | 52 | 2009 |
| Machine learning reveals orbital interaction in materials T Lam Pham, H Kino, K Terakura, T Miyake, K Tsuda, I Takigawa, ... Science and technology of advanced materials 18 (1), 756-765, 2017 | 48 | 2017 |
| Mining significant substructure pairs for interpreting polypharmacology in drug-target network I Takigawa, K Tsuda, H Mamitsuka PloS one 6 (2), e16999, 2011 | 47 | 2011 |
| Dual graph convolutional neural network for predicting chemical networks S Harada, H Akita, M Tsubaki, Y Baba, I Takigawa, Y Yamanishi, ... BMC bioinformatics 21, 1-13, 2020 | 45 | 2020 |
| Analysis of updated literature data up to 2019 on the oxidative coupling of methane using an extrapolative machine‐learning method to identify novel catalysts S Mine, M Takao, T Yamaguchi, T Toyao, Z Maeno, SMA Hakim Siddiki, ... ChemCatChem 13 (16), 3636-3655, 2021 | 43 | 2021 |
