Optimizing data integration improves gene regulatory network inference in Arabidopsis thaliana.

ABSTRACT:

Motivations

Gene regulatory networks (GRNs) are traditionally inferred from gene expression profiles monitoring a specific condition or treatment. In the last decade, integrative strategies have successfully emerged to guide GRN inference from gene expression with complementary prior data. However, datasets used as prior information and validation gold standards are often related and limited to a subset of genes. This lack of complete and independent evaluation calls for new criteria to robustly estimate the optimal intensity of prior data integration in the inference process.

Results

We address this issue for two regression-based GRN inference models, a weighted random forest (weigthedRF) and a generalized linear model estimated under a weighted LASSO penalty with stability

SUBMITTER: Cassan O 

PROVIDER: S-EPMC11227367 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature