Developmental gene regulatory network connections predicted by machine learning from gene expression data alone.

ABSTRACT: Gene regulatory network (GRN) inference can now take advantage of powerful machine learning algorithms to complement traditional experimental methods in building gene networks. However, the dynamical nature of embryonic development-representing the time-dependent interactions between thousands of transcription factors, signaling molecules, and effector genes-is one of the most challenging arenas for GRN prediction. In this work, we show that successful GRN predictions for a developmental network from gene expression data alone can be obtained with the Priors Enriched Absent Knowledge (PEAK) network inference algorithm. PEAK is a noise-robust method that models gene expression dynamics via ordinary differential equations and selects the best network based on information-theoretic criteria c

SUBMITTER: Zhang J 

PROVIDER: S-EPMC8714117 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature