ILSGRN: inference of large-scale gene regulatory networks based on multi-model fusion.

ABSTRACT:

Motivation

Gene regulatory networks (GRNs) are a way of describing the interaction between genes, which contribute to revealing the different biological mechanisms in the cell. Reconstructing GRNs based on gene expression data has been a central computational problem in systems biology. However, due to the high dimensionality and non-linearity of large-scale GRNs, accurately and efficiently inferring GRNs is still a challenging task.

Results

In this article, we propose a new approach, iLSGRN, to reconstruct large-scale GRNs from steady-state and time-series gene expression data based on non-linear ordinary differential equations. Firstly, the regulatory gene recognition algorithm calculates the Maximal Information Coefficient between genes and excludes redundant regulatory r

SUBMITTER: Wu Y 

PROVIDER: S-EPMC10589915 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature