Evolution-informed modeling improves outcome prediction for cancers.

ABSTRACT: Despite wide applications of high-throughput biotechnologies in cancer research, many biomarkers discovered by exploring large-scale omics data do not provide satisfactory performance when used to predict cancer treatment outcomes. This problem is partly due to the overlooking of functional implications of molecular markers. Here, we present a novel computational method that uses evolutionary conservation as prior knowledge to discover bona fide biomarkers. Evolutionary selection at the molecular level is nature's test on functional consequences of genetic elements. By prioritizing genes that show significant statistical association and high functional impact, our new method reduces the chances of including spurious markers in the predictive model. When applied to predicting therapeutic re

SUBMITTER: Liu L 

PROVIDER: S-EPMC5192825 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature