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BIONIC: Biological Network Integration using Convolutions

Duncan T. Forster, Charles Boone, View ORCID ProfileGary D. Bader, Bo Wang
doi: https://doi.org/10.1101/2021.03.15.435515
Duncan T. Forster
1Department of Molecular Genetics, University of Toronto, Toronto ON, Canada
2The Donnelly Centre, University of Toronto, Toronto ON, Canada
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Charles Boone
1Department of Molecular Genetics, University of Toronto, Toronto ON, Canada
2The Donnelly Centre, University of Toronto, Toronto ON, Canada
3Chemical Genomics Research Group, RIKEN Center for Sustainable Resource Sciences, Saitama, Japan
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  • For correspondence: bowang@vectorinstitute.ai
Gary D. Bader
1Department of Molecular Genetics, University of Toronto, Toronto ON, Canada
2The Donnelly Centre, University of Toronto, Toronto ON, Canada
4Department of Computer Science, University of Toronto, Toronto ON, Canada
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  • ORCID record for Gary D. Bader
  • For correspondence: bowang@vectorinstitute.ai
Bo Wang
4Department of Computer Science, University of Toronto, Toronto ON, Canada
5Vector Institute for Artificial Intelligence, Toronto ON, Canada
6Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto ON, Canada
7Peter Munk Cardiac Center, University Health Network, Toronto ON, Canada
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  • For correspondence: bowang@vectorinstitute.ai
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Abstract

Biological networks constructed from varied data, including protein-protein interactions, gene expression data, and genetic interactions can be used to map cellular function, but each data type has individual limitations such as bias and incompleteness. Unsupervised network integration promises to address these limitations by combining and automatically weighting input information to obtain a more accurate and comprehensive result. However, existing unsupervised network integration methods fail to adequately scale to the number of nodes and networks present in genome-scale data and do not handle partial network overlap. To address these issues, we developed an unsupervised deep learning-based network integration algorithm that incorporates recent advances in reasoning over unstructured data – namely the graph convolutional network (GCN) – and can effectively learn dependencies between any input network, such as those composed of protein-protein interactions, gene co-expression, or genetic interactions. Our method, BIONIC (Biological Network Integration using Convolutions), learns features which contain substantially more functional information compared to existing approaches, linking genes that share diverse functional relationships, including co-complex and shared bioprocess annotation. BIONIC is scalable in both size and quantity of the input networks, making it feasible to integrate numerous networks on the scale of the human genome.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://data.wanglab.ml/BIONIC/

  • https://docs.google.com/spreadsheets/d/1B-EsF1zVVa23ssol5p3hYSNUbTOx3q5x_8LEj0ocnIQ/edit?usp=sharing

  • https://docs.google.com/spreadsheets/d/1TKXw-GqklvfR1YU8GBgYOHWJPn-CK4yqJe3WE--g8K4/edit?usp=sharing

  • https://docs.google.com/spreadsheets/d/1UQ1zFnK2PY3ojaYlxU2WELVmubS_6kM2-858-veZSBs/edit?usp=sharing

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 16, 2021.
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BIONIC: Biological Network Integration using Convolutions
Duncan T. Forster, Charles Boone, Gary D. Bader, Bo Wang
bioRxiv 2021.03.15.435515; doi: https://doi.org/10.1101/2021.03.15.435515
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BIONIC: Biological Network Integration using Convolutions
Duncan T. Forster, Charles Boone, Gary D. Bader, Bo Wang
bioRxiv 2021.03.15.435515; doi: https://doi.org/10.1101/2021.03.15.435515

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