This interdisciplinary project aims to develop new statistical and machine learning approaches to analyze high-dimensional, structured and heterogeneous biological data. We focus on the cases where a relatively small number of samples are characterized by huge quantities of quantitative features, a common situation in large-scale genomic projects, but particularly challenging for statistical inference. In order to overcome the curse of dimension we propose to exploit the particular structures of the data, and encode prior biological knowledge in a unified, mathematically sound, and computationally efficient framework. These methodological development, both theoretical and practical, will be guided by and applied to the inference of predictive models and the detection of predictive factors for prognosis and drug response prediction in cancer.
Fields of science
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