Research interests:
Systems Biology
Structured data analysis
Machine Learning
Bio(Chem)informatics

Publications:

B. Bardak and M. Tan. Disease outbreak prediction by data integration and multi-task learning. In IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2017. [ bib ]

M. Tan. Edge distance graph kernel and its application to small molecule classification. Turkish Journal of Electrical Engineering & Computer Sciences, 25(3):2479--2490, 2017. [ bib ]

M. Tan. Prediction of anti-cancer drug response by kernelized multi-task learning. Artificial Intelligence in Medicine, 73:70 -- 77, 2016. [ bib | DOI | http ]

E. Tolan and M. Tan. Anti-cancer drug activity prediction by ensemble learning. In International Conference on Knowledge Discovery and Information Retrieval, 2016. [ bib ]

D. Pancaroglu and M. Tan. Biological network derivation by positive unlabeled learning algorithms. Current Bioinformatics, 11(5):531--536, 2016. [ bib | DOI | http ]

U. Sirin, U. Erdogdu, F. Polat, M. Tan, and R. Alhajj. Effective gene expression data generation framework based on multi-model approach. Artificial Intelligence in Medicine, 70:41--61, 2016. [ bib ]

S. Gao, A. Chen, A. Rahmani, J. Zeng, M. Tan, R. Alhajj, J. Rokne, D. Demetrick, and X. Wei. Multi-scale modularity and motif distributional effect in metabolic networks. Current Protein & Peptide Science, 17(1):82--92, 2016. [ bib | DOI | http ]

B. Bardak and M. Tan. Prediction of Influenza Outbreaks by Integrating Wikipedia Article Access Logs and Google Flu Trend Data. In IEEE International Conference on Bioinformatics & Bioengineering (BIBE 2015), 2015. [ bib ]

Y. Gokcer, M.F. Demirci, and M. Tan. Graph-based Pattern Recognition for Chemical Molecule Matching. In 6th International Conference on Bioinformatics Models, Methods, and Algorithms, 2015. [ bib ]

M. Tan. Drug sensitivity prediction for cancer cell lines based on pairwise kernels and miRNA profiles. In IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2014), pages 156--161, 2014. [ bib ]

D. Pancaroglu and M. Tan. Improving Positive Unlabeled Learning Algorithms for Protein Interaction Prediction. In 8th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2014), pages 81--88, 2014. [ bib ]

U. Erdoğdu, M. Tan, R. Alhajj, F. Polat, J. Rokne, and D. Demetrick. Integrating Machine Learning Techniques into Robust Data Enrichment Approach and Its Application to Gene Expression Data. International Journal of Data Mining and Bioinformatics, 8(3):247--281, 2013. [ bib | DOI ]

M. Tan, R. Alhajj, and F. Polat. Subtree selection in kernels for graph classification. International Journal of Data Mining and Bioinformatics, 8(3):294--310, 2013. [ bib | DOI ]

M. Tan. Information theoretic feature selection for Weisfeiler-Lehman graph kernels. In Proceedings of International Conference on Bioinformatics and Computational Biology (BICOB), 2013. [ bib ]

C. Kiliç and M. Tan. Positive unlabeled learning for deriving protein interaction networks. Network Modeling Analysis in Health Informatics and Bioinformatics, 1(3):87--102, 2012. [ bib ]

M. Alshalalfa, M. Tan, G. Naji, R. Alhajj, F. Polat, and J. Rokne. Revealing miRNA Regulation and miRNA Target Prediction Using Constraint-Based Learning. IEEE Transactions on Systems, Man and Cybernetics, Part C, 42:1354--1364, 2012. [ bib | DOI ]

U. Sirin, U. Erdoğdu, F. Polat, M. Tan, and R. Alhajj. Effective Enrichment of Gene Expression Data Sets. In Proceedings of International Conference on Machine Learning and Applications (ICMLA), 2012. [ bib ]

U. Erdoğdu, M. Tan, R. Alhajj, F. Polat, D. Demetrick, and J. Rokne. Employing Machine Learning Techniques for Data Enrichment: Increasing the number of samples for effective gene expression data analysis. In Proceedings of IEEE International Conference on Bioinformatics & Biomedicine (BIBM), pages 238--242, 2011. [ bib | DOI ]

M. Tan, M. AlShalalfa, R. Alhajj, and F. Polat. Influence of Prior Knowledge in Constraint-Based Learning of Gene Regulatory Networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(1):130--142, 2011. [ bib | DOI ]

M. Tan, R. Alhajj, and F. Polat. Feature Selection for Graph Kernels. In Proceedings of IEEE International Conference on Bioinformatics & Biomedicine (BIBM), pages 632--637, 2010. [ bib | DOI ]

M. Tan, R. Alhajj, and F. Polat. Scalable Approach for Effective Control of Gene Regulatory Networks. Artificial Intelligence in Medicine, 48(1):51--59, 2010. [ bib | DOI ]

M. Tan, R. Alhajj, and F. Polat. Automated Large-Scale Control of Gene Regulatory Networks. IEEE Transactions on Systems, Man and Cybernetics, Part B, 40(2):286--297, 2010. [ bib | DOI ]

M. Tan, F. Polat, and R. Alhajj. Derivation of Transcriptional Regulatory Relationships by Partial Least Squares Regression. In Proceedings of IEEE International Conference on Bioinformatics & Biomedicine (BIBM), 2009. [ bib | DOI ]

M. Tan, M. Alshalalfa, R. Alhajj, and F. Polat. Combining Multiple Types of Biological Data in Constraint-Based Learning of Gene Regulatory Networks. In Proceedings of IEEE Fifth Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2008. [ bib | DOI ]

M. Tan, R. Alhajj, and F. Polat. Large-Scale Approximate Intervention Strategies for Probabilistic Boolean Networks as Models of Gene Regulation. In Proceedings of the IEEE Eighth International Symposium on Bioinformatics and Bioengineering (BIBE), 2008. [ bib | DOI ]

M. Tan, F. Polat, and R. Alhajj. Feature Reduction for Gene Regulatory Network Control. In Proceedings of the IEEE Seventh International Symposium on Bioinformatics and Bioengineering (BIBE), 2007. [ bib | DOI ]


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