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. |
Wenbo Li, Tetsu Matsukawa, Hiroto Saigo, Einoshin Suzuki, Context-Aware Latent Dirichlet Allocation for Topic SegmentationWenbo Li, Tetsu Matsukawa, Hiroto Saigo, Einoshin Suzuki:, PAKDD, 2020.05. |
3. |
Albarakati, H., Saigo, H., Newman, R.H., KC, D.B., SVM-GlutarySite: A support vector machine-based prediction of Glutarylation sites from protein sequences, Joint GIW/ABACBS-2019 Bioinformatics Conference, 2019.09. |
4. |
Thapa, N., Chaudhari, M., McManus, S., Roy, K., Newman, R.H., Saigo, H., KC, D.B., DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction, MCBIOS, 2019.03. |
5. |
Kohei Oyamada and Hiroto Saigo, Bayesian Optimization for Sequence Data, 10th International Conference on Bioscience, Biochemistry and Bioinformatics , 2020.01. |
6. |
Ryoichiro Yafune, Daisuke Sakuma, Yasuo Tabei, Noritaka Saito, Einoshin Suzuki and Hiroto Saigo, A Sparse Bayesian Approach to Combinatorial Feature Selection and Its Applications to Biological Data, 10th International Conference on Bioscience, Biochemistry and Bioinformatics , 2020.01. |
7. |
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。. |
8. |
Hiroto Saigo, Mining and Learning with Structured Data, Japan America Germany Frontiers of Science Symposium, 2017.09. |
9. |
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. |
10. |
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, 文法圧縮されたデータ行列上のスケーラブルなPLS回帰モデル学習を提案した。. |
11. |
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. |
12. |
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. |
13. |
Saigo, H., A Linear Programming Approach for Molecular QSAR analysis ,
Fraunhofer FIRST Seminar, 2006.09. |
14. |
Saigo, H., Partial Least Squares Regression for Graph Mining , Ecole des Mines de Paris and Paris Tech in Paris Seminar, 2008.05. |
15. |
Saigo, H., Incorporating detailed information on treatment history improves prediction of response to anti-HIV therapy , Universitet van Amsterdam Seminar, 2009.12. |
16. |
Saigo, H., Incorporating detailed information on treatment history improves prediction of response to anti-HIV therapy , Eidgenoesische Technische Hochschule (ETH) Zuerich Seminar, 2009.12. |
17. |
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. |
18. |
Saigo, H., Clustering approach to drug discovery, Novartis Animal Health Department Seminar, 2012.12. |
19. |
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. |
20. |
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. |
21. |
Kashima, H., Yamazaki, K., Saigo, H. and Inokuchi, A., Regression with Intervals, International Workshop on Data-Mining and Statistical Science (DMSS2007), 2007.10. |
22. |
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. |
23. |
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. |
24. |
Kashima, H., Yamasaki, K., Saigo, H. and Inokuchi, A., Regression with Interval Output Values, International Conference on Pattern Recognition (ICPR2008), 2008.01. |
25. |
Saigo, H. and Tsuda, K., Iterative Subgraph Mining for Principal Component Analysis, IEEE International Conference on Data Mining (ICDM2008), 2008.12. |
26. |
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. |
27. |
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. |
28. |
Suryanto, C.H., Saigo, H., Fukui, K. , Protein Clustering on Grassman Manifold, Pattern Recognition in Bioinformatics, Pattern Recognition in Bioinformatics (PRIB2012), 2012.11. |
29. |
Saigo, H., Multiple response regression for graph mining , Friedrich Miescher Lab. Seminar, 2013.11. |
30. |
Saigo, H., Multiple response regression for graph mining , Department of Computing Seminar, Imperial College London, 2013.10. |
31. |
Saigo, H., Mining and learning with structured data, BIT2016, 2016.03. |
32. |
Saigo, H., Towards predicting the epistasis in genome wide association study , BMIRC2015, 2015.03. |
33. |
Saigo, H., Towards predicting the epistasis in genome wide association study , BMIRC2015, 2015.03. |
34. |
Saigo, H., Mining discriminative patterns from graph data with multiple labels , BMIRC2014, 2014.01. |
35. |
Saigo, H., Learning from treatment history to predict response to anti-HIV therapy , BMIRC2013, 2013.02. |