Kyushu University Academic Staff Educational and Research Activities Database
List of Presentations
Hiroto Saigo Last modified date:2018.08.01

Associate Professor / Department of Informatics / Faculty of Information Science and Electrical Engineering


Presentations
1. Hiroto Saigo, Mining and Learning with Structured Data, Japan America Germany Frontiers of Science Symposium, 2017.09.
2. Clarence White, Hamid D. Ismail, Hiroto Saigo, K. C. Dukka B., CNN-BLPred: a Convolutional neural network based predictor for β-Lactamases (BL) and their classes, INCOB2017, 2017.09.
3. Saigo, H., A Linear Programming Approach for Molecular QSAR analysis , Fraunhofer FIRST Seminar, 2006.09.
4. Saigo, H., Partial Least Squares Regression for Graph Mining , Ecole des Mines de Paris and Paris Tech in Paris Seminar, 2008.05.
5. Saigo, H., Incorporating detailed information on treatment history improves prediction of response to anti-HIV therapy , Universitet van Amsterdam Seminar, 2009.12.
6. Saigo, H., Incorporating detailed information on treatment history improves prediction of response to anti-HIV therapy , Eidgenoesische Technische Hochschule (ETH) Zuerich Seminar, 2009.12.
7. Saigo, H., Learn- ing from past treatments and their outcome improved prediction of in vivo response to anti-HIV therapy , Ecole des Mines de Paris and Paris Tech in Paris Seminar, 2010.02.
8. Saigo, H., Clustering approach to drug discovery, Novartis Animal Health Department Seminar, 2012.12.
9. 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.
10. 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.
11. Kashima, H., Yamazaki, K., Saigo, H. and Inokuchi, A., Regression with Intervals, International Workshop on Data-Mining and Statistical Science (DMSS2007), 2007.10.
12. 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.
13. 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.
14. Kashima, H., Yamasaki, K., Saigo, H. and Inokuchi, A., Regression with Interval Output Values, International Conference on Pattern Recognition (ICPR2008), 2008.01.
15. Saigo, H. and Tsuda, K., Iterative Subgraph Mining for Principal Component Analysis, IEEE International Conference on Data Mining (ICDM2008), 2008.12.
16. Chiappa, S., Saigo, H. and Tsuda, K., A Bayesian Approach to Graph Regression with Relevant Subgraph Selection, Siam International Conference on Data Mining (SDM2009), 2009.04.
17. 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.
18. Suryanto, C.H., Saigo, H., Fukui, K. , Protein Clustering on Grassman Manifold, Pattern Recognition in Bioinformatics, Pattern Recognition in Bioinformatics (PRIB2012), 2012.11.
19. Saigo, H., Multiple response regression for graph mining , Friedrich Miescher Lab. Seminar, 2013.11.
20. Saigo, H., Multiple response regression for graph mining , Department of Computing Seminar, Imperial College London, 2013.10.
21. Saigo, H., Mining and learning with structured data, BIT2016, 2016.03.
22. Ismail, H.D., Saigo, H., Bahadur, K.C.D., RF-NR: Random forest based approach for improved classification of Nuclear Receptors, The 26th International Conference on Genome Informatics & International Conference on Bioinformatics (GIW/InCoB2015), 2015.09.
23. Saigo, H., Towards predicting the epistasis in genome wide association study , BMIRC2015, 2015.03.
24. Saigo, H., Towards predicting the epistasis in genome wide association study , BMIRC2015, 2015.03.
25. Saigo, H., Mining discriminative patterns from graph data with multiple labels , BMIRC2014, 2014.01.
26. Saigo, H., Learning from treatment history to predict response to anti-HIV therapy , BMIRC2013, 2013.02.
27. 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.
28. Tabei, Y., Saigo, H., Yamanishi, Y., Puglisi, S., Scalable Partial Least Squares Regression on Grammar- Compressed Data Matrices, The 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2016), 2016.08.