A dropout-regularized classifier development approach optimized for precision medicine test discovery from omics data.

ABSTRACT:

Background

Modern genomic and proteomic profiling methods produce large amounts of data from tissue and blood-based samples that are of potential utility for improving patient care. However, the design of precision medicine tests for unmet clinical needs from this information in the small cohorts available for test discovery remains a challenging task. Obtaining reliable performance assessments at the earliest stages of test development can also be problematic. We describe a novel approach to classifier development designed to create clinically useful tests together with reliable estimates of their performance. The method incorporates elements of traditional and modern machine learning to facilitate the use of cohorts where the number of samples is less than the number of measured

SUBMITTER: Roder J 

PROVIDER: S-EPMC6567499 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature