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Supervised-learning is an accurate method for network-based gene classification

View ORCID ProfileRenming Liu, View ORCID ProfileChristopher A Mancuso, View ORCID ProfileAnna Yannakopoulos, View ORCID ProfileKayla A Johnson, View ORCID ProfileArjun Krishnan
doi: https://doi.org/10.1101/721423
Renming Liu
1Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
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Christopher A Mancuso
1Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
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Anna Yannakopoulos
1Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
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Kayla A Johnson
1Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
2Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
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Arjun Krishnan
1Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
2Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
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  • For correspondence: arjun@msu.edu
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Abstract

Background Assigning every human gene to specific functions, diseases, and traits is a grand challenge in modern genetics. Key to addressing this challenge are computational methods such as supervised-learning and label-propagation that can leverage molecular interaction networks to predict gene attributes. In spite of being a popular machine learning technique across fields, supervised-learning has been applied only in a few network-based studies for predicting pathway-, phenotype-, or disease-associated genes. It is unknown how supervised-learning broadly performs across different networks and diverse gene classification tasks, and how it compares to label-propagation, the widely-benchmarked canonical approach for this problem.

Results In this study, we present a comprehensive benchmarking of supervised-learning for network-based gene classification, evaluating this approach and a state-of-the-art label-propagation technique on hundreds of diverse prediction tasks and multiple networks using stringent evaluation schemes. We demonstrate that supervised-learning on a gene’s full network connectivity outperforms label-propagation and achieves high prediction accuracy by efficiently capturing local network properties, rivaling label-propagation’s appeal for naturally using network topology. We further show that supervised-learning on the full network is also superior to learning on node-embeddings (derived using node2vec), an increasingly popular approach for concisely representing network connectivity.

Conclusion These results show that supervised-learning is an accurate approach for prioritizing genes associated with diverse functions, diseases, and traits and should be considered a staple of network-based gene classification workflows. The datasets and the code used to reproduce the results and add new gene classification methods have been made freely available.

Contact arjun{at}msu.edu

Footnotes

  • ↵# These authors are joint first authors and are listed alphabetically

  • https://zenodo.org/record/3352348

  • https://github.com/krishnanlab/GenePlexus

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 4.0 International license.
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Posted August 05, 2019.
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Supervised-learning is an accurate method for network-based gene classification
Renming Liu, Christopher A Mancuso, Anna Yannakopoulos, Kayla A Johnson, Arjun Krishnan
bioRxiv 721423; doi: https://doi.org/10.1101/721423
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Supervised-learning is an accurate method for network-based gene classification
Renming Liu, Christopher A Mancuso, Anna Yannakopoulos, Kayla A Johnson, Arjun Krishnan
bioRxiv 721423; doi: https://doi.org/10.1101/721423

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