Jeff Gaither

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  SNPDogg: Feature-importances in the identification of harmful missense SNPs
  Jeff Gaither
  Recent years have seen an explosion in the use of machine-learning algorithms to classify human mutations. There are now at least 30 scores designed to identify mutations likely to be deleterious to humans, but almost all are "black boxes" that provide no explanation of how they arrived at their predictions. In this talk I'll introduce a new mutational pathogenicity score, SNPDogg, that is transparent, insofar as every prediction can be decomposed as a sum of contributions from the model's features. SNPDogg's feature-importance ​values are computed via a game-theoretic approach implemented in the "shap" python package.
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  Machine Learning in Genomics: Using Models and Not-Models to Explain Genetic Disorders
  Jeff Gaither
  A central problem in genomics is how to determine whether a particular mutation will be harmful to humans. In this task traditional mathematical modeling has proven less successful than machine learning, which pragmatically ?tries to make predictions without regard for biological reasonableness or model elegance. In this talk I'll discuss my transition from applied mathematics to data science, and how I've used machine learning to help pinpoint the causes of genetic disease.