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    Protease target prediction via matrix factorization

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    Author
    Marini, Simone
    Vitali, Francesca
    Rampazzi, Sara
    Demartini, Andrea
    Akutsu, Tatsuya
    Affiliation
    Univ Arizona, Dept Med, BIO5 Inst, Ctr Biomed Informat & Biostat
    Issue Date
    2019-03-15
    
    Metadata
    Show full item record
    Publisher
    OXFORD UNIV PRESS
    Citation
    Simone Marini, Francesca Vitali, Sara Rampazzi, Andrea Demartini, Tatsuya Akutsu, Protease target prediction via matrix factorization, Bioinformatics, Volume 35, Issue 6, 15 March 2019, Pages 923–929, https://doi.org/10.1093/bioinformatics/bty746
    Journal
    BIOINFORMATICS
    Rights
    ©The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Motivation Protein cleavage is an important cellular event, involved in a myriad of processes, from apoptosis to immune response. Bioinformatics provides in silico tools, such as machine learning-based models, to guide the discovery of targets for the proteases responsible for protein cleavage. State-of-the-art models have a scope limited to specific protease families (such as Caspases), and do not explicitly include biological or medical knowledge (such as the hierarchical protein domain similarity or gene-gene interactions). To fill this gap, we present a novel approach for protease target prediction based on data integration. Results By representing protease-protein target information in the form of relational matrices, we design a model (i) that is general and not limited to a single protease family, and (b) leverages on the available knowledge, managing extremely sparse data from heterogeneous data sources, including primary sequence, pathways, domains and interactions. When compared with other algorithms on test data, our approach provides a better performance even for models specifically focusing on a single protease family. Availability and implementation https://gitlab.com/smarini/MaDDA/ (Matlab code and utilized data.) Supplementary information Supplementary data are available at Bioinformatics online.
    Note
    12 month embargo; published: 29 August 2018
    ISSN
    1367-4811
    PubMed ID
    30169576
    DOI
    10.1093/bioinformatics/bty746
    Version
    Final accepted manuscript
    ae974a485f413a2113503eed53cd6c53
    10.1093/bioinformatics/bty746
    Scopus Count
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    UA Faculty Publications

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