Deep learning Methods for Fine Mapping and Discovery in Genomic Association Studies

During this time period, we will develop a suite of novel probabilistic machine learning and deep neural networks tools for fine mapping and discovery in genomic sequencing studies. Specifically, (i) develop an interpretable significance measure for probabilistic machine learning methods, (ii) develop a unified deep learning framework for gene-level and pathway enrichment analysis in genome-wide association studies, and (iii) create distributable software and use it to characterize nonlinear genetic effects at multiple genomic scales in real data applications.

Lorin Crawford Staff 1 Research Assistant i

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