Hiroto Saigo | Last modified date:2023.06.02 |
Associate Professor /
Department of Informatics
Faculty of Information Science and Electrical Engineering
Faculty of Information Science and Electrical Engineering
Graduate School
Undergraduate School
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Homepage
https://kyushu-u.pure.elsevier.com/en/persons/hiroto-saigo
Reseacher Profiling Tool Kyushu University Pure
Phone
092-802-3783
Fax
092-802-3783
Academic Degree
Doctor of Informatics
Country of degree conferring institution (Overseas)
No
Field of Specialization
Machine Learning, Data Mining, Statistics, Bioinformatics, Chemoinformatics
Total Priod of education and research career in the foreign country
05years00months
Outline Activities
His research interest is in developing methods for data mining and artificial intelligence, and applying them to problems in biology and chemistry. He also serves as a program committee member for international conferences in bioinformatics and machine learning.
Research
Research Interests
Membership in Academic Society
- Machine Learning for High-Level Radioactive Waste Management: An Approach Based on Control of High-Temperature Multiphase Melts
keyword : machine learning, high-level radioactive waste, high-temperature multiphase melts
2023.04. - A machine learning approach to automatic design of genes, proteins and chemical compounds
keyword : machine learning, protein squence, chemical compound, design
2022.09. - Development of machine learning methods towards manufacturing informatics
keyword : machine learning, data mining, statistics
2018.04. - Development of a GWAS method that considers interaction among genetic factors and environmental factors
keyword : GWAS, interaction
2013.03. - Development and application of statistical learning methods to the problems associated with Human Immunodeficiency Virus (HIV).
keyword : HIV, statistical learning, pattern mining
2008.06. - Integration of frequent pattern mining with machine learning algorithms
keyword : 頻出パターンマイニング、ブースティング、線形計画法、SVM
2006.06. - Development of kernel methods for detecting remote homology between protein sequences.
keyword : kernel methods, protein homology detection, alignment, SVM
2002.04~2006.03.
Books
Papers
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. |
Presentations
- The Iron and Steel Institute of Japan (ISIJ)
- Japanese Society of Statistics (JSS)
- Japanese Society of Artificial Intelligence (JSAI)
- Japanese Society of Bioinformatics (JSBi)
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