Inferring gene regulatory networks of ALS from blood transcriptome profiles.

ABSTRACT: One of the most robust approaches to the prediction of causal driver genes of complex diseases is to apply reverse engineering methods to infer a gene regulatory network (GRN) from gene expression profiles (GEPs). In this work, we analysed 794 GEPs of 1117 human whole-blood samples from Amyotrophic Lateral Sclerosis (ALS) patients and healthy subjects reported in the GSE112681 dataset. GRNs for ALS and healthy individuals were reconstructed by ARACNe-AP (Algorithm for the Reconstruction of Accurate Cellular Networks - Adaptive Partitioning). In order to examine phenotypic differences in the ALS population surveyed, several datasets were built by arranging GEPs according to sex, spinal or bulbar onset, and survival time. The designed reverse engineering methodology identified a significant

SUBMITTER: Pappalardo XG 

PROVIDER: S-EPMC11648123 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature