Hiroto Saigo | Last modified date：2022.07.05 |

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

**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. | Takayanagi, M., Tabei, Y., Suzuki, E., Saigo, H., Sparse nonnegative interaction models, IEEE Access, 10.1109/ACCESS.2021.3099473, 2021.08, [URL]. |

3. | Li, W. and Saigo, H. and Tong, E. and Suzuki, E., Topic modeling for sequential documents based on hybrid inter-document topic dependency, Journal of Intelligent Information Systems,, 56, 3, 453-458, 2021.06. |

4. | Suryanto, C.H., Saigo, H., Fukui, K. , Protein Clustering on Grassman Manifold, Pattern Recognition in Bioinformatics, Pattern Recognition in Bioinformatics (PRIB2012), 2012.11. |

5. | 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. |

6. | 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. |

7. | Saigo, H. and Tsuda, K., Iterative Subgraph Mining for Principal Component Analysis, IEEE International Conference on Data Mining (ICDM2008), 2008.12. |

8. | Kashima, H., Yamasaki, K., Saigo, H. and Inokuchi, A., Regression with Interval Output Values, International Conference on Pattern Recognition (ICPR2008), 2008.01. |

9. | 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. |

10. | 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. |

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., Kadowaki, T., Kudo, T. and Tsuda, K., Graph boosting for molecular QSAR analysis, NIPS Workshop on Machine Learning in Computational Biology,, 2006.12. |

13. | 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. |

14. | 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. |

15. | 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. |

16. | 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. |

17. | Takayanagi, M., Tabei, Y., Saigo, H., Entire regularization path for sparse nonnegative interaction model, ICDM, 2018.11. |

18. | 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. |

19. | 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. |

20. | 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. |

21. | 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. |

22. | Mirai Takayanagi, Yasuo Tabei, Einoshin Suzuki, Hiroto Saigo, Sparse nonnegative interaction models, IEEE Acess, 10.1109/ACCESS.2021.3099473, 9, 109994-110005, 2021.07, [URL], Non-negative least square regression (NLS) is a constrained least squares problem where the coefficients are restricted to be non-negative. It is useful for modeling non-negative responses such as time measurements, count data, histograms and so on. Existing NLS solvers are designed for cases where the predictor variables and response variables have linear relationships, and do not consider interactions among predictor variables. In this paper, we solve NLS in the complete space of power sets of variables. Such an extension is particularly useful in biology, for modeling genetic associations. Our new algorithms solve NLS problems exactly while decreasing computational burden by using an active set method. The algorithm proceeds in an iterative fashion, such that an optimal interaction term is searched by a branch-and-bound subroutine, and added to the solution set one another. The resulting large search space is efficiently restricted by novel pruning conditions and two kinds of sparsity promoting regularization; l_{1} norm and non-negativity constraints. In computational experiments using HIV-1 datasets, 99% of the search space was safely pruned without losing the optimal variables. In mutagenicity datasets, the proposed method could identify long and accurate patterns compared to the original NLS. Codes are available from https://github.com/afiveithree/inlars .. |

23. | Chaudhari, M., Thapa, N., S., Roy, K., Newman, R.H., Saigo, H., KC, D.B., DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins, Molecular Omics, 16, 448, 2020.10. |

24. | 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,, BMC Bioinformatics, 2020.04. |

25. | Al-barakati, H.J., Thapa, N., Saigo, H., Roy, K., Newman, R.H., Bahadur, K.C.D., RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites, Computational and Structural Biotechnology Journal, 2020.02. |

26. | Al-barakati, H.J., Saigo, H., Newman, R.H., Bahadur, K.C.D., RF-GlutarySite: a random forest predictor for glutarylation sites, Molecular Omics, 15, 189-204, 2019.04. |

27. | 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, BMC Bioinformatics 18(16): 221-232, 2017.12. |

28. | Ismail, H.D., Saigo, H., Bahadur, K.C.D., RF-NR: Random forest based approach for improved classification of Nuclear Receptors, IEEE Transactions on Computational Biology and Bioinformatics , 2017.11, The Nuclear Receptor (NR) superfamily plays an important role in key biological, developmental and physiological processes. Developing a method for the classification of NR proteins is an important step towards understanding the structure and functions of the newly discovered NR protein. The recent studies on NR classification are either unable to achieve optimum accuracy or are not designed for all the known NR subfamilies. In this study we developed RF-NR, which is a Random Forest based approach for improved classification of nuclear receptors. The RF-NR can predict whether a query protein sequence belongs to one of the eight NR subfamilies or it is a non-NR sequence. The RF-NR uses spectrum-like features namely: Amino Acid Composition, Di-peptide Composition and Tripeptide Composition. Benchmarking on two independent datasets with varying sequence redundancy reduction criteria, the RF-NR achieves better (or comparable) accuracy than other existing methods. The added advantage of our approach is that we can also obtain biological insights about the important features that are required to classify NR subfamilies.. |

29. | Suryanto, C. H., Saigo, H., Fukui, K., Protein Structure Comparison Based on 3D Molecular Visualization Images, 2016.08. |

30. | Kodama, K., Saigo, H., KDSNP: a Kernel-based approach to Detecting high-order genetic SNP interactions, 14, 5, 1644003, 2016.10. |

31. | 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. |

32. | Saigo, H., Kashima, H., Tsuda, K., Fast iterative mining using sparsity-inducing loss functions, 96-D, 8, 1766-1773, 2013.08. |

33. | 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. |

34. | 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. |

35. | 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. |

36. | 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. |

37. | Saigo, H., Uno, T. and Tsuda, K., Mining complex genotypic features for predicting HIV-1 drug resistance, 23, 18, 2455-2462, 2007.09. |

38. | Saigo, H., Vert J.-P. and Akutsu, T., Optimizing amino acid substitution matrices with a local alignment kernel, 7, 246, 1-12, 2006.05. |

39. | 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. |

40. | 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. |

41. | 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. |

42. | Ralaivola, L., Swamidass, J. S., Saigo, H. and Baldi, P., Graph Kernels for Chemical Informatics, 18, 8, 1093-1110, 2005.01. |

43. | Saigo, H., Vert, J.-P., Ueda, N. and Akutsu, T., Protein homology detection using string alignment kernels, 20, 11, 1682-1689, 2004.01. |

Unauthorized reprint of the contents of this database is prohibited.