Dataset Information

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 gene is involved in the hormone's production or response. Model training relies on our unified dataset Hormone-Gene version 1 of ground-truth associations between genes and endocrine hormones, which we compiled and carefully balanced in the embedded space to handle data disparities, such as between poorly- versus well-studied hormones. Our BioEmbedS model recapitulates known gene mediators of tissue-tissue signaling with 70.4% accuracy; predicts novel inter-tissue communication genes in humans, which are enriched for hormone-related disorders; and generalizes well to mouse, thereby holding promise for its extension to other multi-cellular organisms as well.

Availability and implementation

Freely available at https://cross-tissue-signaling.herokuapp.com are our model predictions & datasets; https://github.com/BIRDSgroup/BioEmbedS has all relevant code.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Jadhav A

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

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Jadhav Aditya A Kumar Tarun T Raghavendra Mohit M Loganathan Tamizhini T Narayanan Manikandan M

Bioinformatics (Oxford, England) 20221001 20

<h4>Motivation</h4>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.<h4>Results</h4>We present a first study to predict from ...[more]

PMID: 36000859

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Dataset Information

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

Motivation

Results

Availability and implementation

Supplementary information

Publications

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

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