A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

ABSTRACT:

Background

Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility

SUBMITTER: Kang T 

PROVIDER: S-EPMC5735940 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature