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Related Experiment Video

Updated: Aug 31, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Predicting cross-tissue hormone-gene relations using balanced word embeddings.

Aditya Jadhav1, Tarun Kumar1,2,3, Mohit Raghavendra4

  • 1Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India.

Bioinformatics (Oxford, England)
|August 24, 2022
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Summary

This study introduces BioEmbedS, a novel computational model that predicts hormone-gene associations from biomedical literature to map human inter-tissue communication. The model identifies known and novel genes involved in signaling, advancing whole-body systems biology.

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Area of Science:

  • Computational biology
  • Systems biology
  • Genomics

Background:

  • Inter-organ communication is crucial for multicellular organisms, yet mapping tissue interactions from literature remains challenging.
  • Existing literature mining primarily focuses on within-tissue or tissue-agnostic interactions, neglecting inter-tissue signaling pathways like hormone-gene associations.

Purpose of the Study:

  • To develop a computational model for predicting hormone-gene associations from biomedical literature.
  • To identify genes mediating inter-tissue signaling in the human body.
  • To advance whole-body systems-level modeling by mapping endocrine signaling pathways.

Main Methods:

  • Utilized neural network-based Biomedical word Embeddings (BioEmbedS) combined with a Support Vector Machine classifier.
  • Trained the model on a curated and balanced dataset of hormone-gene associations (Hormone-Gene version 1).
  • Evaluated the model's accuracy in predicting known associations and identifying novel mediators.

Main Results:

  • The BioEmbedS model achieved 70.4% accuracy in recapitulating known gene mediators of tissue-tissue signaling.
  • Predicted novel human inter-tissue communication genes, notably those enriched for hormone-related disorders.
  • Demonstrated generalization to mouse models, suggesting broader applicability.

Conclusions:

  • BioEmbedS provides a powerful tool for discovering hormone-gene associations and mapping inter-tissue communication from text.
  • The identified novel genes offer new insights into endocrine signaling and potential therapeutic targets.
  • The model's success holds promise for extending inter-tissue signaling analysis to other multicellular organisms.