Interpretable Machine Learning Methods for Genome-wide Association Mapping

The goal of this project is to build machine learning algorithms and statistical tools that aid in the understanding of how nonlinear interactions between genetic features affect the architecture of complex traits and contribute to disease etiology. A key theme of this work will be to take modern computational approaches and develop theory that enable their interpretations to be related back to classical genomic principles.

Lorin Crawford Staff 1 Research Assistant i

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