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dc.contributor.authorMarini, Simone
dc.contributor.authorVitali, Francesca
dc.contributor.authorRampazzi, Sara
dc.contributor.authorDemartini, Andrea
dc.contributor.authorAkutsu, Tatsuya
dc.date.accessioned2019-04-29T18:58:29Z
dc.date.available2019-04-29T18:58:29Z
dc.date.issued2019-03-15
dc.identifier.citationSimone 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/bty746en_US
dc.identifier.issn1367-4811
dc.identifier.pmid30169576
dc.identifier.doi10.1093/bioinformatics/bty746
dc.identifier.urihttp://hdl.handle.net/10150/632142
dc.description.abstractMotivation 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.en_US
dc.language.isoenen_US
dc.publisherOXFORD UNIV PRESSen_US
dc.rights©The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.titleProtease target prediction via matrix factorizationen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Dept Med, BIO5 Inst, Ctr Biomed Informat & Biostaten_US
dc.identifier.journalBIOINFORMATICSen_US
dc.description.note12 month embargo; published: 29 August 2018en_US
dc.description.collectioninformationThis 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.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleBioinformatics (Oxford, England)


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