American Statistical Association
New York City
Metropolitan Area Chapter

Memorial Sloan-Kettering Cancer Center
Biostatistics Seminar


Shili Lin, Ph.D.
Professor
Department of Statistics
Ohio State University

A CROSS ENTROPY MONTE CARLO APPROACH
FOR AGGREGATING RESULTS
FROM microRNA AND OTHER GENOMIC EXPERIMENTS

MicroRNAs (or miRs) are noncoding RNAs whose role is to repress translation by regulating gene expression through binding to mRNA targets. There are computational algorithms for miR target predictions, but their results vary. Thus, it would be useful to consolidate these results to have a greater degree of certainty before engaging in costly experiments. To this end, a cross-entropy Monte Carlo (CEMC) method is explored for solving this combinatorial optimization problem. In essence, CEMC turns the optimization problem into a problem of estimating rare probabilities, for which an iterative importance sampling technique is utilized to slowly tighten the "net" to place most of the weight on the optimal solution and its neighbors. The results demonstrate that our aggregation method can be a useful tool for short listing genes for downstream experiments. In addition to microRNA, our CEMC rank aggregation method is applicable to a wider class of problems, including aggregation of lists of differentially expressed genes from different mRNA microarray experiments and aggregation of predictions of peptides from mass spectrometry data.

This is joint work with Mr. Jie Ding.


Date: Wednesday, April 18, 2007
Time: 4:00 P.M. - 5:00 P.M.
Location: Memorial Sloan-Kettering Cancer Center
Department of Epidemiology and Biostatistics
307 East 63rd Street
(between First and Second Avenues)
3rd Floor Conference Room
New York, New York
Note: To gain access to the building, please follow the directions by the telephone in the foyer.

RESERVATIONS ARE NOT REQUIRED


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