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GNE: a deep learning framework for gene network inference by aggregating biological information.

Kishan Kc1, Rui Li2, Feng Cui3

  • 1Golisano College of Computing and Information Sciences, Rochester Institute of Technology, 20 Lomb Memorial Drive, Rochester, New York, 14623, USA. kk3671@rit.edu.

BMC Systems Biology
|April 7, 2019
PubMed
Summary

This study introduces a deep learning framework to integrate gene expression and interaction data for improved gene network inference. The model achieves more accurate predictions of gene interactions, with novel findings validated by existing databases.

Keywords:
Deep learningGene expressionGene interaction networksHeterogeneous data integrationNetwork embedding

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Gene interaction networks offer insights into gene function but integrating diverse data remains challenging.
  • Unifying heterogeneous biological information like gene expression and interactions is key for accurate gene network inference.
  • Developing a unified vector representation for diverse gene data is a critical challenge in bioinformatics.

Purpose of the Study:

  • To develop a scalable deep learning framework for unifying gene expression and interaction data.
  • To generate low-dimensional embedded representations for enhanced gene network inference.
  • To improve the accuracy of predicting gene interactions and discovering novel ones.

Main Methods:

  • A novel deep learning framework (GNE) was developed to learn unified vector representations.
  • The framework integrates gene expression and known gene interaction data.
  • Embeddings were generated to simplify downstream modeling and analysis of gene networks.

Main Results:

  • The proposed deep embeddings significantly improved the accuracy of gene interaction predictions compared to baseline methods.
  • Novel gene interactions were predicted and subsequently validated using up-to-date literature-based databases.
  • The framework demonstrated the value of integrating heterogeneous gene information for network inference.

Conclusions:

  • The developed deep learning model effectively integrates diverse gene information for robust gene network inference.
  • The framework provides a powerful tool for uncovering functional patterns and predicting gene interactions.
  • The GNE model is publicly available for broader research use in computational biology.