Inferring gene regulatory networks of ALS from blood transcriptome profiles

• Published in: Heliyon Vol. 10; no. 23; p. e40696
• Main Authors: Pappalardo, Xena G., Jansen, Giorgio, Amaradio, Matteo, Costanza, Jole, Umeton, Renato, Guarino, Francesca, De Pinto, Vito, Oliver, Stephen G., Messina, Angela, Nicosia, Giuseppe
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15.12.2024; Elsevier
• Subjects: Algorithm for the reconstruction of accurate cellular networks - adaptive partitioning version (ARACNe-AP); algorithms; amyotrophic lateral sclerosis; Amyotrophic lateral sclerosis (ALS); biomarkers; blood; data collection; females; gene expression; Gene expression profile (GEP); Gene regulatory network (GRN); gene regulatory networks; genes; humans; phenotype; prediction; Reverse engineering methods; transcription (genetics); transcriptome; Gene expression profile (GEP); Gene regulatory network (GRN); Amyotrophic lateral sclerosis (ALS); Reverse engineering methods; Algorithm for the reconstruction of accurate cellular networks - adaptive partitioning version (ARACNe-AP)

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...