GkmExplain: fast and accurate interpretation of nonlinear gapped k-mer SVMs.

ABSTRACT:

Summary

Support Vector Machines with gapped k-mer kernels (gkm-SVMs) have been used to learn predictive models of regulatory DNA sequence. However, interpreting predictive sequence patterns learned by gkm-SVMs can be challenging. Existing interpretation methods such as deltaSVM, in-silico mutagenesis (ISM) or SHAP either do not scale well or make limiting assumptions about the model that can produce misleading results when the gkm kernel is combined with nonlinear kernels. Here, we propose GkmExplain: a computationally efficient feature attribution method for interpreting predictive sequence patterns from gkm-SVM models that has theoretical connections to the method of Integrated Gradients. Using simulated regulatory DNA sequences, we show that GkmExplain identifies predictive patt

SUBMITTER: Shrikumar A 

PROVIDER: S-EPMC6612808 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature