Globally learning gene regulatory networks based on hidden atomic regulators from transcriptomic big data.

ABSTRACT:

Background

Genes are regulated by various types of regulators and most of them are still unknown or unobserved. Current gene regulatory networks (GRNs) reverse engineering methods often neglect the unknown regulators and infer regulatory relationships in a local and sub-optimal manner.

Results

This paper proposes a global GRNs inference framework based on dictionary learning, named dlGRN. The method intends to learn atomic regulators (ARs) from gene expression data using a modified dictionary learning (DL) algorithm, which reflects the whole gene regulatory system, and predicts the regulation between a known regulator and a target gene in a global regression way. The modified DL algorithm fits the scale-free property of biological network, rendering dlGRN intrinsically disce

SUBMITTER: Shi M 

PROVIDER: S-EPMC7559338 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature