BIONIC (Biological Network Integration using Convolutions) is a scalable deep learning network integration approach that learns and combines diverse data representations across a range of biological network types to consolidate knowledge of gene function. BIONIC outperforms existing integration approaches by capturing biological information more comprehensively and with greater accuracy than previously possible.
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References
Mitra, K. et al. Integrative approaches for finding modular structure in biological networks. Nat. Rev. Genet. 14, 719–732 (2013). This review article outlines the importance of biological network integration.
Fraser, A. G. & Marcotte, E. M. A probabilistic view of gene function. Nat. Genet. 36, 559 (2004). This perspectives article outlines the utility of biological networks.
Costanzo, M. et al. A global genetic interaction network maps a wiring diagram of cellular function. Science 353, aaf1420 (2016). This paper describes the global yeast genetic interaction network, which served as an input network for BIONIC.
Veličković, P. et al. Graph attention networks. Preprint at arXiv https://doi.org/10.48550/arXiv.1710.10903 (2017). This paper describes the graph attention network used in BIONIC.
Piotrowski, J. S. et al. Functional annotation of chemical libraries across diverse biological processes. Nat. Chem. Biol. 13, 982–993 (2017). This paper describes a high-throughput chemical–genetic screening approach and data that were used by BIONIC to generate chemical–genetic interaction predictions.
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This is a summary of: Forster, D. T. et al. BIONIC: biological network integration using convolutions. Nat. Methods https://doi.org/10.1038/s41592-022-01616-x (2022).
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BIONIC: discovering new biology through deep learning-based network integration. Nat Methods 19, 1185–1186 (2022). https://doi.org/10.1038/s41592-022-01617-w
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DOI: https://doi.org/10.1038/s41592-022-01617-w
