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Probabilistic Methods in Cancer Biology

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If you have a question about this talk, please contact Dr Jason Z JIANG.

In this talk I will discuss two specific problems in cancer biology, namely: Identifying the most informative features, and reverse-engineering genome-wide interaction networks. The first is a non-standard problem in machine learning, wherein the number of features is many times larger than the number of samples, the inverse of the usual situation in engineering. The second is a problem of constructing a minimal weighted directed graph that is consistent with the data. For each problem, I will discuss new and appropriate algorithms invented by my team, and their application/validation in three forms of cancer: lung, ovarian and endometrial. I will also suggest a broad framework through which engineers can make meaningful contributions to cancer biology, and suggest a few problems for future research.

This talk is part of the CUED Control Group Seminars series.

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