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Hiroto Saigo Last modified date:2019.07.26



Graduate School
Undergraduate School


E-Mail
Phone
092-802-3783
Fax
092-802-3783
Academic Degree
Doctor of Informatics
Field of Specialization
Machine Learning, Data Mining, Statistics, Bioinformatics, Chemoinformatics
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
  • 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. Suryanto, C. H., Saigo, H., Fukui, K. , Protein Structure Comparison Based on 3D Molecular Visualization Images , 2017.01.
2. Saigo, H., Vert, J.-P., Ueda, N. and Akutsu, T., Protein homology detection using string alignment kernels, 20, 11, 1682-1689, 2004.01.
3. Ralaivola, L., Swamidass, J. S., Saigo, H. and Baldi, P., Graph Kernels for Chemical Informatics, 18, 8, 1093-1110, 2005.01.
4. 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.
5. 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.
6. 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.
7. Saigo, H., Vert J.-P. and Akutsu, T., Optimizing amino acid substitution matrices with a local alignment kernel, 7, 246, 1-12, 2006.05.
8. Saigo, H., Uno, T. and Tsuda, K., Mining complex genotypic features for predicting HIV-1 drug resistance, 23, 18, 2455-2462, 2007.09.
9. 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.
10. 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.
11. 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.
12. 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.
13. Kodama, K., Saigo, H., KDSNP: a Kernel-based approach to Detecting high-order genetic SNP interactions, 14, 5, 1644003, 2016.10.
14. 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.
15. Saigo, H., Kashima, H., Tsuda, K., Fast iterative mining using sparsity-inducing loss functions, 96-D, 8, 1766-1773, 2013.08.
Presentations
1. 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。.
2. 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.
3. 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.
4. Saigo, H., Mining and learning with structured data, BIT2016, 2016.03.
5. Saigo, H., Towards predicting the epistasis in genome wide association study , BMIRC2015, 2015.03.
6. Saigo, H., Towards predicting the epistasis in genome wide association study , BMIRC2015, 2015.03.
Membership in Academic Society
  • JSS
  • JSAI
  • JSBi