Statistical learning with genomic big-data

第十届中国R会议【直播】

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The number of publicly available gene expression and genome sequence datasets has been growing dramatically. Various methods have been proposed to predict gene functions by integrating the publicly available datasets. I will use a few recent projects we carried out to illustrate the roles and importance of statistical modeling for extracting knowledge (i.e., learning) from genomic and genetic big-data and to predict treatment effects from genomic information. The take-home lessons are (a) statistical models are all “wrong” in certain technical aspects, but are extremely useful for synthesizing information (much more so than ``techniques”-driven approaches); (b) statistical thinking is important for understanding predictions and observational findings; (c) Bayesian-like data integration and model averaging can provide more coherent and accurate answers to intricate problems. As examples, we show that our algorithm CLIC is capable of integrating over thousands of gene expression datasets to achieve much higher co-expression prediction accuracy compared to traditional co-expression methods. We also show that statistical learning model-based personalized chemotherapy selection results in significant higher survival rates compared with standard practice for breast cancer patients.

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