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Journal Articles
- 2023
[j19]Pawel Pratyush
, Suresh Pokharel, Hiroto Saigo, Dukka B. KC:
pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model. BMC Bioinform. 24(1): 41 (2023)- 2022
- [j18]Niraj Thapa, Meenal Chaudhari, Sean McManus, Kaushik Roy, Robert H. Newman, Hiroto Saigo, K. C. Dukka B.:
Correction: DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction. BMC Bioinform. 23(1): 349 (2022) - 2021
- [j17]Mirai Takayanagi, Yasuo Tabei, Einoshin Suzuki, Hiroto Saigo:
Sparse Nonnegative Interaction Models. IEEE Access 9: 109994-110005 (2021) - [j16]Wenbo Li, Hiroto Saigo, Bin Tong, Einoshin Suzuki:
Topic modeling for sequential documents based on hybrid inter-document topic dependency. J. Intell. Inf. Syst. 56(3): 435-458 (2021) - 2020
- [j15]Niraj Thapa, Meenal Chaudhari, Sean McManus, Kaushik Roy, Robert H. Newman, Hiroto Saigo, Dukka B. KC:
DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction. BMC Bioinform. 21-S(3): 63 (2020) - 2018
- [j14]Chendra Hadi Suryanto, Hiroto Saigo, Kazuhiro Fukui:
Structural Class Classification of 3D Protein Structure Based on Multi-View 2D Images. IEEE ACM Trans. Comput. Biol. Bioinform. 15(1): 286-299 (2018) - [j13]Hamid D. Ismail, Hiroto Saigo, K. C. Dukka B.:
RF-NR: Random Forest Based Approach for Improved Classification of Nuclear Receptors. IEEE ACM Trans. Comput. Biol. Bioinform. 15(6): 1844-1852 (2018) - 2017
- [j12]Clarence White, Hamid D. Ismail, Hiroto Saigo, K. C. Dukka B.:
CNN-BLPred: a Convolutional neural network based predictor for β-Lactamases (BL) and their classes. BMC Bioinform. 18(16): 221-232 (2017) - 2016
- [j11]Kento Kodama, Hiroto Saigo:
KDSNP: A kernel-based approach to detecting high-order SNP interactions. J. Bioinform. Comput. Biol. 14(5): 1644003:1-1644003:16 (2016) - 2015
- [j10]Zheng Shao, Yuya Hirayama, Yoshihiro Yamanishi, Hiroto Saigo:
Mining Discriminative Patterns from Graph Data with Multiple Labels and Its Application to Quantitative Structure-Activity Relationship (QSAR) Models. J. Chem. Inf. Model. 55(12): 2519-2527 (2015) - 2013
- [j9]Hiroto Saigo, Hisashi Kashima, Koji Tsuda:
Fast Iterative Mining Using Sparsity-Inducing Loss Functions. IEICE Trans. Inf. Syst. 96-D(8): 1766-1773 (2013) - 2011
- [j8]Yoshihiro Yamanishi, Edouard Pauwels, Hiroto Saigo, Véronique Stoven:
Extracting Sets of Chemical Substructures and Protein Domains Governing Drug-Target Interactions. J. Chem. Inf. Model. 51(5): 1183-1194 (2011) - 2010
- [j7]Hiroto Saigo, Masahiro Hattori, Hisashi Kashima, Koji Tsuda:
Reaction graph kernels predict EC numbers of unknown enzymatic reactions in plant secondary metabolism. BMC Bioinform. 11(S-1): 31 (2010) - 2009
- [j6]Hiroto Saigo, Sebastian Nowozin, Tadashi Kadowaki, Taku Kudo, Koji Tsuda:
gBoost: a mathematical programming approach to graph classification and regression. Mach. Learn. 75(1): 69-89 (2009) - 2007
- [j5]Hiroto Saigo, Takeaki Uno, Koji Tsuda:
Mining complex genotypic features for predicting HIV-1 drug resistance. Bioinform. 23(18): 2455-2462 (2007) - 2006
- [j4]Hiroto Saigo, Jean-Philippe Vert, Tatsuya Akutsu:
Optimizing amino acid substitution matrices with a local alignment kernel. BMC Bioinform. 7: 246 (2006) - [j3]Samuel A. Danziger, Sanjay Joshua Swamidass, Jue Zeng, Lawrence R. Dearth, Qiang Lu, Jonathan H. Chen, Jianlin Cheng, Vinh P. Hoang, Hiroto Saigo, Ray Luo, Pierre Baldi, Rainer K. Brachmann, Richard H. Lathrop:
Functional Census of Mutation Sequence Spaces: The Example of p53 Cancer Rescue Mutants. IEEE ACM Trans. Comput. Biol. Bioinform. 3(2): 114-125 (2006) - 2005
- [j2]Liva Ralaivola, Sanjay Joshua Swamidass, Hiroto Saigo, Pierre Baldi:
Graph kernels for chemical informatics. Neural Networks 18(8): 1093-1110 (2005) - 2004
- [j1]Hiroto Saigo, Jean-Philippe Vert, Nobuhisa Ueda, Tatsuya Akutsu:
Protein homology detection using string alignment kernels. Bioinform. 20(11): 1682-1689 (2004)
Conference and Workshop Papers
- 2024
- [c11]Kotaro Ii, Hiroto Saigo, Yasuo Tabei:
A Branch-and-Bound Approach to Efficient Classification and Retrieval of Documents. ICPRAM 2024: 205-214 - [c10]Kendai Itakura, Dukka Bahadur, Hiroto Saigo:
Benchmarking a Wide Range of Unsupervised Learning Methods for Detecting Anomaly in Blast Furnace. ICPRAM 2024: 641-650 - 2020
- [c9]Ryoichiro Yafune, Daisuke Sakuma, Mirai Takayanagi, Yasuo Tabei, Noritaka Saito, Hiroto Saigo:
Automatically mining Relevant Variable Interactions Via Sparse Bayesian Learning. ICPR 2020: 9635-9642 - [c8]Wenbo Li, Tetsu Matsukawa, Hiroto Saigo, Einoshin Suzuki:
Context-Aware Latent Dirichlet Allocation for Topic Segmentation. PAKDD (1) 2020: 475-486 - 2018
- [c7]Mirai Takayanagi, Yasuo Tabei, Hiroto Saigo:
Entire Regularization Path for Sparse Nonnegative Interaction Model. ICDM 2018: 1254-1259 - 2016
- [c6]Yasuo Tabei, Hiroto Saigo, Yoshihiro Yamanishi, Simon J. Puglisi:
Scalable Partial Least Squares Regression on Grammar-Compressed Data Matrices. KDD 2016: 1875-1884 - 2012
- [c5]Chendra Hadi Suryanto, Hiroto Saigo, Kazuhiro Fukui:
Protein Clustering on a Grassmann Manifold. PRIB 2012: 71-81 - 2009
- [c4]Silvia Chiappa, Hiroto Saigo, Koji Tsuda:
A Bayesian Approach to Graphy Regression with Relevant Subgraph Selection. SDM 2009: 295-304 - 2008
- [c3]Hiroto Saigo, Koji Tsuda:
Iterative Subgraph Mining for Principal Component Analysis. ICDM 2008: 1007-1012 - [c2]Hisashi Kashima, Kazutaka Yamasaki, Akihiro Inokuchi, Hiroto Saigo:
Regression with interval output values. ICPR 2008: 1-4 - [c1]Hiroto Saigo, Nicole Krämer, Koji Tsuda:
Partial least squares regression for graph mining. KDD 2008: 578-586
Parts in Books or Collections
- 2011
- [p2]Hiroto Saigo, Koji Tsuda:
Matrix Decomposition-based Dimensionality Reduction on Graph Data. Graph Data Management 2011: 260-284 - 2010
- [p1]Koji Tsuda, Hiroto Saigo:
Graph Classification. Managing and Mining Graph Data 2010: 337-363
Informal and Other Publications
- 2016
- [i1]Yasuo Tabei, Hiroto Saigo, Yoshihiro Yamanishi, Simon J. Puglisi:
Scalable Partial Least Squares Regression on Grammar-Compressed Data Matrices. CoRR abs/1606.05031 (2016)
Coauthor Index
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