Publications
Journal Articles
- Pratyush, P., Pokharel, S., Saigo, H., KC.D.B.: 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 (02 2023) PDF
- Saigo, H., Bahadur, K.C.D, Saito, N.: Einstein-Roscoe regression for the slag viscosity prediction problem in steelmaking, Scientific Reports, Vol 12, Article number: 6541 (04 2022), PDF
- Takayanagi, M., Tabei, Y., Suzuki, E., Saigo, H.: Sparse nonnegative interaction models, IEEE Access, Vol 9, pp. 109994-110005, (08 2021). DOI: 10.1109/ACCESS.2021.3099473, PDF
- Li, W. and Saigo, H. and Tong, E. and Suzuki, E.: Topic modeling for sequential documents based on hybrid inter-document topic dependency, Journal of Intelligent Information Systems, Vol 56(3), 453-458, (06 2021)
- Chaudhari, M., Thapa, N., S., Roy, K., Newman, R.H., Saigo, H., KC, D.B.: DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins, Molecualar Omics, 16, 448, (10 2020),
- Thapa, N., Chaudhari, M., McManus, S., Roy, K., Newman, R.H., Saigo, H., KC, D.B.: DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction, BMC Bioinformatics, 21(Suppl 3):63, (04 2020), PDF
- Al-barakati, H.J., Thapa, N., Saigo, H., Roy, K., Newman, R.H., Bahadur, K.C.D.: RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites , Computational and Structural Biotechnology Journal, Vol 18. pp. 852-860, (02 2020), PDF
- Al-barakati, H.J., Saigo, H., Newman, R.H., Bahadur, K.C.D.: RF-GlutarySite: a random forest predictor for glutarylation sites , Molecular Omics, Vol 15. pp. 189-204, (04 2019), PDF
- White, C., Ismail, H., Saigo, H., Bahadur, K.C.D.: CNN-BLPred: A Convolutional Neural Network based predictor for Beta-Lactamases (BL) and their classes , BMC Bioinformatics 18(Suppl 16):577, 2017. PDF
- Ismail, H.D., Saigo, H., Bahadur, K.C.D.: RF-NR: Random forest based approach for improved classification of Nuclear Receptors , IEEE Transactions on Computational Biology and Bioinformatics , Vol.15, Issue:6 (11 2018). PDF
- Kodama, K., Saigo, H.: KDSNP: a Kernel-based approach to Detecting high-order SNP interactions Journal of Bioinformatics and Computational Biology 14(5), 1644003, 2016. PDF Software
- Suryanto, C. H., Saigo, H., Fukui, K.: Structural Class Classification of 3D Protein Structure Based on Multi-View 2D Images IEEE Transactions on Computational Biology and Bioinformatics , Vol.15, Issue:1 pp.286-299(08 2016). PDF
- Shao, Z., Hirayama, Y., Yamanishi, Y., Saigo, H.: Mining discriminative patterns from graph data with multiple labels and its application to QSAR Journal of Chemical Information and Modeling , 55(12), 2519-27 (12 2015). PDF Software
- Saigo, H., Kashima. H., Tsuda, K.: Fast iterative mining using sparsity-inducing loss functions , IEICE Transaction on Information and Systems, Vol.E96-D No.8 pp.1766-1773 (08 2013). PDF
- Yamanishi, Y., Pauwels, E., Saigo, H., Stoven, V.: Extracting sets of chemical substructures and protein domains governing drug-target interactions, Journal of Chemical Information and Modeling, 51(5), 1183-1194, (05 2011). PDF
- Saigo, H., Altmann, A., Bogojeska, J., Mueller, F., Nowozin, S., and Lengauer, T.: Learning from past treatments and their outcome improves prediction of in vivo response to anti-HIV therapy , Statistical Applications in Genetics and Molecular Biology 10(1) (01 2011). PDF Software
- Saigo, H., Hattori, M., Kashima, H., and Tsuda, K.: Reaction graph kernels that predict EC numbers of unknown enzymatic reactions in the secondary metabolism of plant, BMC Bioinformatics 11 (Supple 1), 1-7, (01 2010), PDF Data
- Saigo, H., Nowozin, S., Kadowaki, T., Kudo, T., and Tsuda, K.: gBoost: A mathematical programming approach to graph classification and regression, Machine Learning 75(1) 69-89, (04 2009). PDF Data Software
- Saigo, H., Uno, T. and Tsuda, K.: Mining complex genotypic features for predicting HIV-1 drug resistance, Bioinformatics 23(18), 2455-2462 (09 2007) PDF Data Software
- Saigo, H., Vert J.-P. and Akutsu, T.: Optimizing amino acid substitution matrices with a local alignment kernel, BMC Bioinformatics 7(246), 1-12 (05 2006) PDF Software
- Danziger, S. A., Swamidass, S. J., Zeng, J., Dearth, L. R., Lu, Q., Cheng, J. H., Cheng, J. L., Hoang, V. P., Saigo, H., Luo, R., Baldi, P., Brachmann, R. K. and Lathrop, R. H.: Functional census of mutation sequence spaces: The example of p53 cancer rescue mutants, IEEE Transactions on Computational Biology and Bioinformatics 3(2), 114-125 (04 2006) PDF
- Cheng, J., Saigo, H. and Baldi, P.: Large-scale prediction of disulphide bridges using kernel methods, two-dimensional recursive neural networks, and weighted graph matching, Proteins 62(3), 617-629 (02 2006) PDF
- Matsuda, S., Vert, J.-P., Saigo, H., Ueda, N., Toh, H. and Akutsu, T.: A novel representation of protein sequences for prediction of subcellular location using support vector machines, Protein Science 14, 2804-2813 (2005) PDF
- Ralaivola, L., Swamidass, J. S., Saigo, H. and Baldi, P.: Graph Kernels for Chemical Informatics, Neural Networks 18(8), 1093-1110 (2005) PDF
- Saigo, H., Vert, J.-P., Ueda, N. and Akutsu, T.: Protein homology detection using string alignment kernels, Bioinformatics 20(11), 1682-1689 (2004) PDF Data and Software
Conference Papers
- Itakura, K., KC, D.B., Saigo, H.: Benchmarking a wide range of unsupervised learning methods for detecting anomaly in blast furnace; International Conference of Pattern Recognition Applications and Methods (ICPRAM2024), Rome, February 24-26, 2024.
- Ii, K., Tabei, Y., Saigo, H.: A branch-and-bound approach to efficient classification and retrieval of documents; International Conference of Pattern Recognition Applications and Methods (ICPRAM2024), Rome, February 24-26, 2024.
- Yafune, R., Sakuma, D., Tabei, Y., Saito, N., Saigo, H.: Automatically mining relevant variable interactions via sparse Bayesian learning; International Conference of Pattern Recognition (ICPR2020), Milan, January 15-20, 2021.
- Li, W., Matsukawa, T., Saigo, H., Suzuki, E.: Context-Aware Latent Dirichlet Allocation for Topic Segmentation; Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2020), Singapore, May 14, 2020.
- Thapa, N., Chaudhari, M., McManus, S., Roy, K., Newman, R.H., Saigo, H., KC, D.B.: DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction, MCBIOS, Birmingham, March 28-30, 2019.
- Albarakati, H., Saigo, H., Newman, R.H., KC, D.B.: SVM-GlutarySite: A support vector machine-based prediction of Glutarylation sites from protein sequences, Joint GIW/ABACBS-2019 Bioinformatics Conference, Sydney, December 9-11, 2019.
- Takayanagi, M., Tabei, Y., Saigo, H.: Entire regularization path for sparse nonnegative interaction model,
International Conference of Data Mining (ICDM2018), Singapore, November 17-20, 2018.
- White, C., Ismail, H., Saigo, H., Bahadur, K.C.D.: CNN-BLPred: A Convolutional Neural Network based predictor for Beta-Lactamases (BL) and their classes , International Conference on Bioinformatics (InCoB2017) , Shenzhen, China, September 20-22, 2017.
- Tabei, Y., Saigo, H., Yamanishi, Y., Puglisi, S., Scalable Partial Least Squares Regression on Grammar-
Compressed Data Matrices , ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2016) , 1875-1884, San Francisco, U.S., August 13-17, 2016.
- Kodama, K., Saigo, H.: KDSNP: a Kernel-based approach to Detecting high-order genetic Epistasis International Conference on Genome Informatics (GIW2016), Shanghai, China, October 3-5, 2016.
- Ismail, H.D., Saigo, H., Bahadur, K.C.D., RF-NR: Random forest based approach for improved classification of Nuclear Receptors , International Conference on Genome Informatics & International Conference on Bioinformatics (GIW/InCoB2015), Tokyo, Japan. (9 2015).
- Suryanto, C.H., Saigo, H., Fukui, K.: Protein Clustering on Grassman Manifold, Pattern Recognition in Bioinformatics (PRIB2012), Tokyo, Japan. (11 2012).
- Saigo, H., Hattori, M., Kashima, H., and Tsuda, K.: Reaction graph kernels that predict EC numbers of unknown enzymatic reactions in the secondary metabolism of plant, In Proceedings of the eight Asia Pacifc Bioinformatics Conference (APBC2010), Bangalore, India, (1 2010).
- Chiappa, S., Saigo, H. and Tsuda, K.: A Bayesian Approach to Graph Regression with Relevant Subgraph Selection In Proceedings of the Siam International Conference on Data Mining (SDM2009), Nevada, U.S. (4 2009) PDF
- Saigo, H. and Tsuda, K.: Iterative Subgraph Mining for Principal Component Analysis In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM2008), 1007-1012, Pisa , Italy. (12 2008)Data PDF
- Kashima, H., Yamasaki, K., Saigo, H. and Inokuchi, A.: Regression with Interval Output Values, In Proc. 19th International Conference on Pattern Recognition (ICPR2008), 2008. PDF
- Saigo, H., Kraemer, N. and Tsuda, K.: Partial Least Squares Regression for Graph Mining, In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2008), 578-586, Las Vegas, U.S. (08 2008) PDF Data Software
- Saigo, H., M. Hattori and K. Tsuda: Reaction graph kernels for discovering missing enzymes in the plant secondary metabolism, NIPS Workshop on Machine Learning in Computational Biology, Whistler, BC, Canada (12 2007)
- Kashima, H., Yamazaki, K., Saigo, H. and Inokuchi, A.: Regression with Intervals, International Workshop on Data-Mining and Statistical Science (DMSS2007), Tokyo, Japan (10 2007) JSAI Incentive Award
- Saigo, H., Kadowaki, T., Kudo, T. and Tsuda, K.: Graph boosting for molecular QSAR analysis, NIPS Workshop on Machine Learning in Computational Biology, Whistler, BC, Canada (12 2006).Data
- Saigo, H., Kadowaki, T. and Tsuda, K.: A Linear Programming Approach for Molecular QSAR analysis, International Workshop on Mining and Learning with Graphs (MLG2006), 85-96, Berlin, Germany (9 2006) Best Paper Award PDF Data
Book Chapters
- Pakhrin, S.C. and Pokharel, S. and Saigo, H. and B. Kc, D. :Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction 285-322, In Methods Mol Biol Springer, (2022), doi: 10.1007/978-1-0716-2317-6_15.
- Saigo, H. and Tsuda, K.: Matrix Decomposition-based Dimensionality Reduction on Graph Data 260-284, In Sakr, S. and Pardede, E. editors Graph Data Management: Techniques and Applications IGI Global, (2011)
- Tsuda, K. and Saigo, H.: Graph Classification 337-364, In Sakr, C.C.C. and Wang, H. editors Managing and Mining Graph Data, Springer, (2010)
- Kashima, H., Saigo, H., Hattori, M. and Tsuda, K. Graph Kernels for Chemoinformatics 1-15, In Lodhi, H and Yamanishi, Y. editors Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Method and Collaborative Techniques IGI Global (2010)
- Saigo, H. and Tsuda, K. Graph Mining for Chemoinformatics 95-128, In Lodhi, H and Yamanishi, Y. editors Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Method and Collaborative Techniques IGI Global (2010)
- Vert, J.-P., Saigo, H. and Akutsu, T.: Local Alignment Kernels for Biological Sequences, 131-153, In Bernhard Scheolkopf, Koji Tsuda and Jean-Philippe Vert editors, Kernel Methods in Computational Biology MIT Press, Cambridge, MA, (2004)
PhD Theses
- Saigo, H.: Local Alignment Kernels for Protein Homology Detection. (2006)
Softwares