Enhanced identification of significant regulators of gene expression.

ABSTRACT: BACKGROUND:Diseases like cancer will lead to changes in gene expression, and it is relevant to identify key regulatory genes that can be linked directly to these changes. This can be done by computing a Regulatory Impact Factor (RIF) score for relevant regulators. However, this computation is based on estimating correlated patterns of gene expression, often Pearson correlation, and an assumption about a set of specific regulators, normally transcription factors. This study explores alternative measures of correlation, using the Fisher and Sobolev metrics, and an extended set of regulators, including epigenetic regulators and long non-coding RNAs (lncRNAs). Data on prostate cancer have been used to explore the effect of these modifications. RESULTS:A tool for computation of RIF scores with

SUBMITTER: Ehsani R 

PROVIDER: S-EPMC7132893 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature