Predicting cross-tissue hormone-gene relations using balanced word embeddings.

ABSTRACT:

Motivation

Inter-organ/inter-tissue communication is central to multi-cellular organisms including humans, and mapping inter-tissue interactions can advance system-level whole-body modeling efforts. Large volumes of biomedical literature have fostered studies that map within-tissue or tissue-agnostic interactions, but literature-mining studies that infer inter-tissue relations, such as between hormones and genes are solely missing.

Results

We present a first study to predict from biomedical literature the hormone-gene associations mediating inter-tissue signaling in the human body. Our BioEmbedS* models use neural network-based Biomedical word Embeddings with a Support Vector Machine classifier to predict if a hormone-gene pair is associated or not, and whether an associated

SUBMITTER: Jadhav A 

PROVIDER: S-EPMC9563690 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature