Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference.

ABSTRACT:

Background

Modeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of genome-wide transcription factor activity (TFA) making it difficult to separate covariance and regulatory interactions. Inference of regulatory interactions and TFA requires aggregation of complementary evidence. Estimating TFA explicitly is problematic as it disconnects GRN inference and TFA estimation and is unable to account for, for example, contextual transcription factor-transcription factor interactions, and other higher order features. Deep-learning offers a potential solution, as it can model complex interactions and higher-order latent features, although does not provide interpretable models and latent features.

Results

We propose a novel autoencoder-based fram

SUBMITTER: Tjarnberg A 

PROVIDER: S-EPMC10797903 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature