AGRN: accurate gene regulatory network inference using ensemble machine learning methods.

ABSTRACT:

Motivation

Biological processes are regulated by underlying genes and their interactions that form gene regulatory networks (GRNs). Dysregulation of these GRNs can cause complex diseases such as cancer, Alzheimer's and diabetes. Hence, accurate GRN inference is critical for elucidating gene function, allowing for the faster identification and prioritization of candidate genes for functional investigation. Several statistical and machine learning-based methods have been developed to infer GRNs based on biological and synthetic datasets. Here, we developed a method named AGRN that infers GRNs by employing an ensemble of machine learning algorithms.

Results

From the idea that a single method may not perform well on all datasets, we calculate the gene importance scores using thr

SUBMITTER: Alawad DM 

PROVIDER: S-EPMC10082608 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature