Pathway-Based Genomics Prediction Using Generalized Elastic Net

Overview

Journal PLoS Comput Biol

Specialty Biology

Date 2016 Mar 10

PMID 26960204

Citations 52

Authors

Artem Sokolov

Daniel E Carlin

Evan O Paull

Robert Baertsch

Joshua M Stuart

Affiliations

Soon will be listed here.

Abstract

We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach.

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