Prediction of key gene function in spinal muscular atrophy using guilt by association method based on network and gene ontology.

ABSTRACT: Guilt by association (GBA) algorithm has been widely used to predict gene functions statistically, and a network-based approach may increase the confidence and veracity of identifying molecular signatures for diseases. The aim of the present study was to suggest a gene ontology (GO)-based method by integrating the GBA algorithm and network, to identify key gene functions for spinal muscular atrophy (SMA). The inference of predicting key gene functions was comprised of four steps, preparing gene lists and sets; extracting differentially expressed genes (DEGs) using microarray data [linear models for microarray data (limma)] package; constructing a co-expression matrix on gene lists using the Spearman correlation coefficient method; and predicting gene functions by GBA algorithm. Ultimately,

SUBMITTER: Yang W 

PROVIDER: S-EPMC6425128 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature