Graph kernels for chemical informatics L Ralaivola, SJ Swamidass, H Saigo, P Baldi Neural networks 18 (8), 1093-1110, 2005 | 597 | 2005 |
Protein homology detection using string alignment kernels H Saigo, JP Vert, N Ueda, T Akutsu Bioinformatics 20 (11), 1682-1689, 2004 | 501 | 2004 |
A novel representation of protein sequences for prediction of subcellular location using support vector machines S Matsuda, JP Vert, H Saigo, N Ueda, H Toh, T Akutsu Protein Science 14 (11), 2804-2813, 2005 | 189 | 2005 |
Large‐scale prediction of disulphide bridges using kernel methods, two‐dimensional recursive neural networks, and weighted graph matching J Cheng, H Saigo, P Baldi Proteins: Structure, Function, and Bioinformatics 62 (3), 617-629, 2006 | 167 | 2006 |
gBoost: a mathematical programming approach to graph classification and regression H Saigo, S Nowozin, T Kadowaki, T Kudo, K Tsuda Machine Learning 75, 69-89, 2009 | 160 | 2009 |
Partial least squares regression for graph mining H Saigo, N Krämer, K Tsuda Proceedings of the 14th ACM SIGKDD international conference on knowledge …, 2008 | 108 | 2008 |
Local alignment kernels for biological sequences JP Vert, H Saigo, T Akutsu Kernel methods in computational biology, 131-154, 2004 | 101 | 2004 |
Extracting sets of chemical substructures and protein domains governing drug-target interactions Y Yamanishi, E Pauwels, H Saigo, V Stoven Journal of chemical information and modeling 51 (5), 1183-1194, 2011 | 93 | 2011 |
Mining complex genotypic features for predicting HIV-1 drug resistance H Saigo, T Uno, K Tsuda Bioinformatics 23 (18), 2455-2462, 2007 | 68 | 2007 |
Optimizing amino acid substitution matrices with a local alignment kernel H Saigo, JP Vert, T Akutsu BMC bioinformatics 7, 1-12, 2006 | 67 | 2006 |
Functional census of mutation sequence spaces: the example of p53 cancer rescue mutants SA Danziger, SJ Swamidass, J Zeng, LR Dearth, Q Lu, JH Chen, J Cheng, ... IEEE/ACM transactions on computational biology and bioinformatics 3 (2), 114-125, 2006 | 65 | 2006 |
DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction N Thapa, M Chaudhari, S McManus, K Roy, RH Newman, H Saigo, DB Kc BMC bioinformatics 21, 1-10, 2020 | 54 | 2020 |
RF-GlutarySite: a random forest based predictor for glutarylation sites HJ Al-Barakati, H Saigo, RH Newman Molecular omics 15 (3), 189-204, 2019 | 37 | 2019 |
Graph classification K Tsuda, H Saigo Managing and mining graph data, 337-363, 2010 | 35 | 2010 |
CNN-BLPred: a convolutional neural network based predictor for β-lactamases (BL) and their classes C White, HD Ismail, H Saigo, DB Kc BMC bioinformatics 18, 221-232, 2017 | 29 | 2017 |
Scalable partial least squares regression on grammar-compressed data matrices Y Tabei, H Saigo, Y Yamanishi, SJ Puglisi Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge …, 2016 | 28 | 2016 |
A linear programming approach for molecular QSAR analysis H Saigo, T Kadowaki, K Tsuda International Workshop on Mining and Learning with Graphs 2006 (MLG 2006), 85-96, 2009 | 28 | 2009 |
DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins M Chaudhari, N Thapa, K Roy, RH Newman, H Saigo, BKC Dukka Molecular omics 16 (5), 448-454, 2020 | 23 | 2020 |
Local alignment kernels for protein sequences V Jean-Philippe Kernel methods in computational biology, 2004 | 23 | 2004 |
Kyushu University T Zendo Japan, 0 | 21 | |