Kyushu University [Hiroto Saigo (Associate Professor) Faculty of Information Science and Electrical Engineering, Department of Informatics]

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Researcher information

Hiroto Saigo
Associate Professor
Department of Informatics
Faculty of Information Science and Electrical Engineering
Last modified date：2023.06.02

Graduate School

Department of informatics
Graduate School of Information Science and Electrical Engineering

Undergraduate School

Department of Physics
School of Sciences

Department of Electrical Engineering and Computer Science
School of Engineering

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Academic Degree

Country of degree conferring institution (Overseas)

Field of Specialization

Total Priod of education and research career in the foreign country

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Research

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1.	Saigo, H., Tsuda, K., Graph Mining for Chemoinformatics In Lodhi, H and Yamanishi, Y. editors Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Method and Collaborative Technique, 95-128, 2010.01.
2.	Kashima, H., Saigo, H., Hattori, M., Tsuda, K., Graph Kernels for Chemoinformatics In Lodhi, H and Yamanishi, Y. editors Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Method and Collaborative Techniques, IGI Global, 1-15, 2010.01.
3.	Saigo, H., Tsuda, K., Graph Classification In Sakr, C.C.C. and Wang, H. editors Managing and Mining Graph Data, Springer, 337-364, 2010.01.
4.	Saigo, H., Tsuda, K., Matrix Decomposition-based Dimensionality Reduction on Graph Data In Sakr, S. and Pardede, E. editors Graph Data Management: Techniques and Applications, IGI Global, 260-284, 2011.01.

1.	Saigo, H., Bahadur, K.C.D, Saito, N., Einstein-Roscoe regression for the slag viscosity prediction problem in steelmaking, Scientific Reports, 12, 2022.04, [URL].
2.	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, Asia Pacific Bioinformatics Conference (APBC2010), 2010.01.
3.	Saigo, H. and Tsuda, K., Iterative Subgraph Mining for Principal Component Analysis, IEEE International Conference on Data Mining (ICDM2008), 2008.12.
4.	Saigo, H., Kraemer, N. and Tsuda, K., Partial Least Squares Regression for Graph Mining, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2008), 2008.08.
5.	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,, 2007.12.
6.	Saigo, H., Kadowaki, T., Kudo, T. and Tsuda, K., Graph boosting for molecular QSAR analysis, NIPS Workshop on Machine Learning in Computational Biology,, 2006.12.
7.	Saigo, H., Kadowaki, T. and Tsuda, K., A Linear Programming Approach for Molecular QSAR analysis, International Workshop on Mining and Learning with Graphs (MLG2006), 2006.09.
8.	Kodama, K., Saigo, H., KDE: a Kernel-based approach to detecting high-order genetic Epistasis, The 27th International Conference on Genome Informatics (GIW2016), 2016.10.
9.	Yafune, R., Sakuma, D., Tabei, Y., Saito, N., Saigo, H., Automatically mining relevant variable interactions via sparse Bayesian learning, International Conference on Pattern Recognition , 2021.01.
10.	Suryanto, C. H., Saigo, H., Fukui, K., Protein Structure Comparison Based on 3D Molecular Visualization Images, 2016.08.
11.	Kodama, K., Saigo, H., KDSNP: a Kernel-based approach to Detecting high-order genetic SNP interactions, 14, 5, 1644003, 2016.10.
12.	Shao, Z., Hirayama, Y., Yamanishi, Y., Saigo, H., Mining discriminative patterns from graph data with multiple labels and its application to QSAR, 55, 12, 2519-2527, 2015.12.
13.	Saigo, H., Kashima, H., Tsuda, K., Fast iterative mining using sparsity-inducing loss functions, 96-D, 8, 1766-1773, 2013.08.
14.	Yamanishi, Y., Pauwels, E., Saigo, H., Stoven, V. , Extracting sets of chemical substructures and protein domains governing drug-target interactions , 51, 5, 1183-1194, 2011.05.
15.	Saigo, H., Altmann, A., Bogojeska, J., Mueller, F., Nowozin, S., and Lengauer, T. , Learnig from past treatments and their outcome improves prediction of in vivo response t anti-HIV therapy , 10, 1, 2011.01.
16.	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, 11(supple 1), 1-7, 2010.10.
17.	Saigo, H., Nowozin, S., Kadowaki, T., Kudo, T., and Tsuda, K., gBoost: A mathematical programming approach to graph classification and regression, 75, 1, 69-89, 2009.04.
18.	Saigo, H., Uno, T. and Tsuda, K., Mining complex genotypic features for predicting HIV-1 drug resistance, 23, 18, 2455-2462, 2007.09.
19.	Saigo, H., Vert J.-P. and Akutsu, T., Optimizing amino acid substitution matrices with a local alignment kernel, 7, 246, 1-12, 2006.05.
20.	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, 3, 2, 114-125, 2006.04.
21.	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,, 62, 3, 617-629, 2006.02.
22.	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, 14, 2804-2813, 2005.01.
23.	Ralaivola, L., Swamidass, J. S., Saigo, H. and Baldi, P., Graph Kernels for Chemical Informatics, 18, 8, 1093-1110, 2005.01.
24.	Saigo, H., Vert, J.-P., Ueda, N. and Akutsu, T., Protein homology detection using string alignment kernels, 20, 11, 1682-1689, 2004.01.

1.	Yafune, R., Sakuma, D., Tabei, Y., Saito, N., Saigo, H., Automatically mining relevant variable interactions via sparse Bayesian learning, International Conference on Pattern Recognition , 2021.01.
2.	Takayanagi, M., Tabei, Y., Saigo, H., Entire regularization path for sparse nonnegative interaction model, International Conference on Data Mining (ICDM), 2018.11, 本研究では相互作用を考慮した非負値最小二乗法に対する正則化パス追跡アルゴリズムを提案した。組み合わせ空間の効率的な探索のための枝刈りを実装した本手法は、LASSOよりも大幅に小さい解集合を得られることを計算機実験で示した。 HIVデータを用いた実験では、重要な遺伝子要因の組み合わせを自動的に探索することにより、薬剤耐性モデルを正確に推定出来ることを示した。.
3.	Kodama, K., Saigo, H., KDE: a Kernel-based approach to detecting high-order genetic Epistasis, The 27th International Conference on Genome Informatics (GIW2016), 2016.10.
4.	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, Asia Pacific Bioinformatics Conference (APBC2010), 2010.01.
5.	Saigo, H., Mining and learning with structured data, BIT2016, 2016.03.
6.	Saigo, H., Towards predicting the epistasis in genome wide association study , BMIRC2015, 2015.03.
7.	Saigo, H., Towards predicting the epistasis in genome wide association study , BMIRC2015, 2015.03.