Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection.

ABSTRACT: Despite extensive research efforts, reconstruction of gene regulatory networks (GRNs) from transcriptomics data remains a pressing challenge in systems biology. While non-linear approaches for reconstruction of GRNs show improved performance over simpler alternatives, we do not yet have understanding if joint modelling of multiple target genes may improve performance, even under linearity assumptions. To address this problem, we propose two novel approaches that cast the GRN reconstruction problem as a blend between regularized multivariate regression and graphical models that combine the L2,1-norm with classical regularization techniques. We used data and networks from the DREAM5 challenge to show that the proposed models provide consistently good performance in comparison to contenders w

SUBMITTER: Mbebi AJ 

PROVIDER: S-EPMC10414675 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature