| Graph kernels for chemical informatics L Ralaivola, SJ Swamidass, H Saigo, P Baldi Neural networks 18 (8), 1093-1110, 2005 | 644 | 2005 |
| Protein homology detection using string alignment kernels H Saigo, JP Vert, N Ueda, T Akutsu Bioinformatics 20 (11), 1682-1689, 2004 | 503 | 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 | 198 | 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 | 174 | 2006 |
| gBoost: a mathematical programming approach to graph classification and regression H Saigo, S Nowozin, T Kadowaki, T Kudo, K Tsuda Machine Learning 75 (1), 69-89, 2009 | 170 | 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 | 112 | 2008 |
| Local alignment kernels for biological sequences JP Vert, H Saigo, T Akutsu Kernel methods in computational biology, 131-154, 2004 | 109 | 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 | 99 | 2011 |
| 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 (Suppl 3), 63, 2020 | 77 | 2020 |
| Optimizing amino acid substitution matrices with a local alignment kernel H Saigo, JP Vert, T Akutsu BMC bioinformatics 7 (1), 246, 2006 | 70 | 2006 |
| Mining complex genotypic features for predicting HIV-1 drug resistance H Saigo, T Uno, K Tsuda Bioinformatics 23 (18), 2455-2462, 2007 | 65 | 2007 |
| 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 |
| RF-GlutarySite: a random forest based predictor for glutarylation sites HJ Al-Barakati, H Saigo, RH Newman, DB Kc Molecular omics 15 (3), 189-204, 2019 | 43 | 2019 |
| Graph classification K Tsuda, H Saigo Managing and mining graph data, 337-363, 2010 | 43 | 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 (Suppl 16), 577, 2017 | 36 | 2017 |
| DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins M Chaudhari, N Thapa, K Roy, RH Newman, H Saigo, D BKC Molecular omics 16 (5), 448-454, 2020 | 34 | 2020 |
| pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model P Pratyush, S Pokharel, H Saigo, DB Kc BMC bioinformatics 24 (1), 41, 2023 | 32 | 2023 |
| 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 | 31 | 2016 |
| Deep learning–based advances in protein posttranslational modification site and protein cleavage prediction SC Pakhrin, S Pokharel, H Saigo, DB Kc Computational methods for predicting post-translational modification sites …, 2022 | 27 | 2022 |
| 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 | 27 | 2009 |
Koji TsudaProfessor, GSFS, The University of TokyoVerified email at k.u-tokyo.ac.jp
