PFBNet: a priori-fused boosting method for gene regulatory network inference.

ABSTRACT:

Background

Inferring gene regulatory networks (GRNs) from gene expression data remains a challenge in system biology. In past decade, numerous methods have been developed for the inference of GRNs. It remains a challenge due to the fact that the data is noisy and high dimensional, and there exists a large number of potential interactions.

Results

We present a novel method, namely priori-fused boosting network inference method (PFBNet), to infer GRNs from time-series expression data by using the non-linear model of Boosting and the prior information (e.g., the knockout data) fusion scheme. Specifically, PFBNet first calculates the confidences of the regulation relationships using the boosting-based model, where the information about the accumulation impact of the gene express

SUBMITTER: Che D 

PROVIDER: S-EPMC7362553 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature