Supervised consensus scoring for docking and virtual screening R Teramoto, H Fukunishi Journal of chemical information and modeling 47 (2), 526-534, 2007 | 128 | 2007 |
Antibody design using LSTM based deep generative model from phage display library for affinity maturation K Saka, T Kakuzaki, S Metsugi, D Kashiwagi, K Yoshida, M Wada, ... Scientific reports 11 (1), 5852, 2021 | 124 | 2021 |
Global gene expression analysis in liver of obese diabetic db/db mice treated with metformin M Heishi, J Ichihara, R Teramoto, Y Itakura, K Hayashi, H Ishikawa, ... Diabetologia 49, 1647-1655, 2006 | 81 | 2006 |
Prediction of siRNA functionality using generalized string kernel and support vector machine R Teramoto, M Aoki, T Kimura, M Kanaoka FEBS letters 579 (13), 2878-2882, 2005 | 80 | 2005 |
A unique gene expression signature discriminates familial Alzheimer's disease mutation carriers from their wild-type siblings Y Nagasaka, K Dillner, H Ebise, R Teramoto, H Nakagawa, L Lilius, ... Proceedings of the National Academy of Sciences 102 (41), 14854-14859, 2005 | 63 | 2005 |
Protein expression profile characteristic to hepatocellular carcinoma revealed by 2D-DIGE with supervised learning R Teramoto, H Minagawa, M Honda, K Miyazaki, Y Tabuse, K Kamijo, ... Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics 1784 (5), 764-772, 2008 | 50 | 2008 |
Consensus scoring with feature selection for structure-based virtual screening R Teramoto, H Fukunishi Journal of chemical information and modeling 48 (2), 288-295, 2008 | 48 | 2008 |
Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target S Chiba, K Ikeda, T Ishida, MM Gromiha, YH Taguchi, M Iwadate, ... Scientific reports 5 (1), 17209, 2015 | 46 | 2015 |
Comparative proteomic and transcriptomic profiling of the human hepatocellular carcinoma H Minagawa, M Honda, K Miyazaki, Y Tabuse, R Teramoto, T Yamashita, ... Biochemical and biophysical research communications 366 (1), 186-192, 2008 | 38 | 2008 |
An iterative compound screening contest method for identifying target protein inhibitors using the tyrosine-protein kinase Yes S Chiba, T Ishida, K Ikeda, M Mochizuki, R Teramoto, YH Taguchi, ... Scientific Reports 7 (1), 12038, 2017 | 33 | 2017 |
Prediction of protein–ligand binding affinities using multiple instance learning R Teramoto, H Kashima Journal of Molecular Graphics and Modelling 29 (3), 492-497, 2010 | 21 | 2010 |
A prospective compound screening contest identified broader inhibitors for Sirtuin 1 S Chiba, M Ohue, A Gryniukova, P Borysko, S Zozulya, N Yasuo, ... Scientific reports 9 (1), 19585, 2019 | 20 | 2019 |
Bootstrap-based consensus scoring method for protein–ligand docking H Fukunishi, R Teramoto, T Takada, J Shimada Journal of chemical information and modeling 48 (5), 988-996, 2008 | 17 | 2008 |
A method for clustering gene expression data based on graph structure S Seno, R Teramoto, Y Takenaka, H Matsuda Genome Informatics 15 (2), 151-160, 2004 | 17 | 2004 |
Structure-based virtual screening with supervised consensus scoring: evaluation of pose prediction and enrichment factors R Teramoto, H Fukunishi Journal of chemical information and modeling 48 (4), 747-754, 2008 | 14 | 2008 |
Estimating causal effects with a non-paranormal method for the design of efficient intervention experiments R Teramoto, C Saito, S Funahashi BMC bioinformatics 15, 1-14, 2014 | 13 | 2014 |
Balanced gradient boosting from imbalanced data for clinical outcome prediction R Teramoto Statistical applications in genetics and molecular biology 8 (1), 2009 | 13 | 2009 |
Hidden Active Information in a Random Compound Library: Extraction Using a Pseudo-Structure− Activity Relationship Model H Fukunishi, R Teramoto, J Shimada Journal of chemical information and modeling 48 (3), 575-582, 2008 | 13 | 2008 |
Supervised scoring models with docked ligand conformations for structure-based virtual screening R Teramoto, H Fukunishi Journal of chemical information and modeling 47 (5), 1858-1867, 2007 | 13 | 2007 |
Prediction of Alzheimer's diagnosis using semi-supervised distance metric learning with label propagation R Teramoto Computational biology and chemistry 32 (6), 438-441, 2008 | 11 | 2008 |