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    Using Deep Maxout Neural Networks to improve the accuracy of function prediction from Protein Interaction Networks

    Wan, Cen and Cozzetto, D. and Fa, R. and Jones, D. (2019) Using Deep Maxout Neural Networks to improve the accuracy of function prediction from Protein Interaction Networks. PLoS One , ISSN 1932-6203.

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    Abstract

    Protein-protein interaction network data provides valuable information that infers direct links between genes and their biological roles. This information brings a fundamental hypothesis for protein function prediction that interacting proteins tend to have similar functions. With the help of recently-developed network embedding feature generation methods and deep maxout neural networks, it is possible to extract functional representations that encode direct links between protein-protein interactions information and protein function. Our novel method, STRING2GO, successfully adopts deep maxout neural networks to learn functional representations simultaneously encoding both protein-protein interactions and functional predictive information. The experimental results show that STRING2GO outperforms other protein-protein interaction network-based prediction methods and one benchmark method adopted in a recent large scale protein function prediction competition.

    Metadata

    Item Type: Article
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Cen Wan
    Date Deposited: 23 Oct 2019 17:46
    Last Modified: 30 Oct 2019 23:00
    URI: http://eprints.bbk.ac.uk/id/eprint/29618

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