A Bayesian network structure learning approach to identify genes associated with stress in spleens of chickens.

ABSTRACT: Differences in the expression patterns of genes have been used to measure the effects of non-stress or stress conditions in poultry species. However, the list of genes identified can be extensive and they might be related to several biological systems. Therefore, the aim of this study was to identify a small set of genes closely associated with stress in a poultry animal model, the chicken (Gallus gallus), by reusing and combining data previously published together with bioinformatic analysis and Bayesian networks in a multi-step approach. Two datasets were collected from publicly available repositories and pre-processed. Bioinformatics analyses were performed to identify genes common to both datasets that showed differential expression patterns between non-stress and stress conditions. Ba

SUBMITTER: Videla Rodriguez EA 

PROVIDER: S-EPMC9076669 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature