Kyushu University Academic Staff Educational and Research Activities Database
Researcher information (To researchers) Need Help? How to update
Hiroto Saigo Last modified date:2023.06.02



Graduate School
Undergraduate School


E-Mail *Since the e-mail address is not displayed in Internet Explorer, please use another web browser:Google Chrome, safari.
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
  • 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.
Academic Activities
Books
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.
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
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.
Membership in Academic Society
  • The Iron and Steel Institute of Japan (ISIJ)
  • Japanese Society of Statistics (JSS)
  • Japanese Society of Artificial Intelligence (JSAI)
  • Japanese Society of Bioinformatics (JSBi)