Multi-tissue DNA methylation microarray signature is predictive of gene function.

ABSTRACT: Background Transcriptional correlation networks derived from publicly available gene expression microarrays have been previously shown to be predictive of known gene functions, but less is known about the predictive capacity of correlated DNA methylation at CpG sites. Guilt-by-association co-expression methods can adapted for use with DNA methylation when a representative methylation value is created for each gene. We examine how methylation compares to expression in predicting Gene Ontology terms using both co-methylation and traditional machine learning approaches across different types of representative methylation values per gene. Methods We perform guilt-by-association gene function prediction with a suite of models called Methylation Array Network Analysis, using a network of correla

SUBMITTER: Roopnarinesingh XR 

PROVIDER: S-EPMC9586602 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature