LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data.

ABSTRACT:

Motivation

From a systematic perspective, it is crucial to infer and analyze gene regulatory network (GRN) from high-throughput single-cell RNA sequencing data. However, most existing GRN inference methods mainly focus on the network topology, only few of them consider how to explicitly describe the updated logic rules of regulation in GRNs to obtain their dynamics. Moreover, some inference methods also fail to deal with the over-fitting problem caused by the noise in time series data.

Results

In this article, we propose a novel embedded Boolean threshold network method called LogBTF, which effectively infers GRN by integrating regularized logistic regression and Boolean threshold function. First, the continuous gene expression values are converted into Boolean values and th

SUBMITTER: Li L 

PROVIDER: S-EPMC10172039 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature