Biomarker discovery for predicting spontaneous preterm birth from gene expression data by regularized logistic regression.

ABSTRACT: In this work, we provide a computational method of regularized logistic regression for discovering biomarkers of spontaneous preterm birth (SPTB) from gene expression data. The successful identification of SPTB biomarkers will greatly benefit the interference of infant gestational age for reducing the risks of pregnant women and preemies. In recent years, various approaches have been proposed for the feature selection of identifying the subset of meaningful genes that can achieve accurate classification for disease samples from controls. Here, we comprehensively summarize the regularized logistic regression with seven effective penalties developed for the selection of strongly indicative genes of SPTB from microarray data. We compare their properties and assess their classification perform

SUBMITTER: Li L 

PROVIDER: S-EPMC7689379 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature