Research Interests

• Machine Learning: Statistical Learning, Graphical Models, and Optimization
• Bioinformatics: Sequence Analysis, Gene Regulation, Association Studies, and Personal Genomics

Publications

The ENCODE Project Consortium. An Integrated Encyclopedia of DNA Elements in the Human Genome. Nature. In revision.

Gerstein et al.. Analysis of the human regulatory network using ENCODE data. Nature. In revision.

C. Cheng, R. Alexander, R. Min, ..., E. Birney, Z. Weng, M. Gerstein. Understanding transcriptional regulation by integrative analysis of transcription factor binding data. Genome Research. Under review.

C. Cheng, R. Min, and M. Gerstein. A probabilistic method for identifying transcription factor target genes from ChIP-Seq binding profiles. Bioinformatics. 2011.

K. Jin, J. Li, F. Vizeacoumar, Z. Li, R. Min, L. Zamparo, F. Vizeacoumar, A. Datti, B. Andrews, C. Boone, and Z. Zhang. PhenoM: a database of morphological phenotypes caused by mutation of essential genes in Saccharomyces cerevisiae. Nucleic Acids Research, Database Issue. 2011.

Z. Li, F. Vizeacoumar, S. Bahr, J. Li, J. Waringer,F. Vizeacoumar, R. Min, ..., Z. Zhang, A. Blomberg, K. Bloom, B. Andrews, C. Boone (31 co-authors). Systematic exploration of essential yeast gene function with temperature-sensitive mutants. Nature Biotechnology. 2011.

R. Min. Machine learning approaches to biological sequence and phenotype data analysis. PhD Thesis. Department of Computer Science, University of Toronto. September 2010.

J. Li, Y. Liu, T. Kim, R. Min, and Z. Zhang. Gene expression variability within and between human populations and implications toward disease susceptibility. PLoS Computational Biology. 2010.

J. Li, R. Min, FJ Vizeacoumar, K. Jin, X. Xin, and Z. Zhang. Exploiting the determinants of stochastic gene expression in S. cerevisiae for genome-wide prediction of expression noise. Proc Natl Acad Sci U S A (PNAS). Vol. 107 No. 23, 10472-10477. 2010.

R. Min, L. van der Maaten, Z. Yuan, A. Bonner, and Z. Zhang. Deep supervised t-distributed embedding. The 27th International Conference on Machine Learning (ICML 2010), Haifa, Israel, 2010. [Slides][Source code]

R. Min, D. A. Stanley, Z. Yuan, A. Bonner, and Z. Zhang. A deep non-linear feature mapping for large-margin kNN classification. IEEE International Conference on Data Mining (ICDM 2009). PP. 357-366. Regular paper and oral presentation. Acceptance rate: 70/786.[Slides][Source code].

R. Min, A. Bonner, J. Li, and Z. Zhang. Learned random-walk kernels and empirical-map kernels for protein sequence classification. Journal of Computational Biology. March 2009, 16(3): 457-474.

J. Li*, R. Min*, A. Bonner, and Z. Zhang. (*Co-first authors) A probabilistic framework to improve microRNA target prediction by incorporating proteomics data. Journal of Bioinformatics and Computational Biology. Volume: 7, Issue: 6(2009) pp. 955-972.

R. Min, R. Kuang, A. Bonner, and Z. Zhang. Learning random-walk kernels for protein remote homology identification and motif discovery. 2009 SIAM International Conference on Data Mining (SDM09). PP. 133-144. Full paper and oral presentation. Acceptance rate: 55/351.

R. Min, A. Bonner, and Z. Zhang. Modifying kernels using label information improves SVM classification performance. IEEE Proceeding of the 2007 International Conference on Machine Learning and Applications, pp. 13-18. December 13-15, 2007.

R. Min. A non-linear dimensionality reduction method for improving nearest neighbour classification. Master Thesis. Department of Computer Science, University of Toronto. 2005.

Technical Report