Mining hidden knowledge: embedding models of cause–effect relationships curated from the biomedical literature

ABSTRACT: Abstract

Motivation

We explore the use of literature-curated signed causal gene expression and gene–function relationships to construct unsupervised embeddings of genes, biological functions and diseases. Our goal is to prioritize and predict activating and inhibiting functional associations of genes and to discover hidden relationships between functions. As an application, we are particularly interested in the automatic construction of networks that capture relevant biology in a given disease context.

Results

We evaluated several unsupervised gene embedding models leveraging literature-curated signed causal gene expression findings. Using linear regression, we show that, based on these gene embeddings, gene–function relationships can be predicted with about 95% precision

SUBMITTER: Kramer A 

PROVIDER: S-EPMC9710590 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature