Inferring structural and dynamical properties of gene networks from data with deep learning.

ABSTRACT: The reconstruction of gene regulatory networks (GRNs) from data is vital in systems biology. Although different approaches have been proposed to infer causality from data, some challenges remain, such as how to accurately infer the direction and type of interactions, how to deal with complex network involving multiple feedbacks, as well as how to infer causality between variables from real-world data, especially single cell data. Here, we tackle these problems by deep neural networks (DNNs). The underlying regulatory network for different systems (gene regulations, ecology, diseases, development) can be successfully reconstructed from trained DNN models. We show that DNN is superior to existing approaches including Boolean network, Random Forest and partial cross mapping for network infere

SUBMITTER: Chen F 

PROVIDER: S-EPMC9469930 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature