Show simple item record

dc.contributor.authorBoudellioua, Imene
dc.contributor.authorSaidi, Rabie
dc.contributor.authorHoehndorf, Robert
dc.contributor.authorMartin, Maria J.
dc.contributor.authorSolovyev, Victor
dc.date.accessioned2016-08-01T10:33:26Z
dc.date.available2016-08-01T10:33:26Z
dc.date.issued2016-07-08
dc.identifier.citationPrediction of Metabolic Pathway Involvement in Prokaryotic UniProtKB Data by Association Rule Mining 2016, 11 (7):e0158896 PLOS ONE
dc.identifier.issn1932-6203
dc.identifier.pmid27390860
dc.identifier.doi10.1371/journal.pone.0158896
dc.identifier.urihttp://hdl.handle.net/10754/617797
dc.description.abstractThe widening gap between known proteins and their functions has encouraged the development of methods to automatically infer annotations. Automatic functional annotation of proteins is expected to meet the conflicting requirements of maximizing annotation coverage, while minimizing erroneous functional assignments. This trade-off imposes a great challenge in designing intelligent systems to tackle the problem of automatic protein annotation. In this work, we present a system that utilizes rule mining techniques to predict metabolic pathways in prokaryotes. The resulting knowledge represents predictive models that assign pathway involvement to UniProtKB entries. We carried out an evaluation study of our system performance using cross-validation technique. We found that it achieved very promising results in pathway identification with an F1-measure of 0.982 and an AUC of 0.987. Our prediction models were then successfully applied to 6.2 million UniProtKB/TrEMBL reference proteome entries of prokaryotes. As a result, 663,724 entries were covered, where 436,510 of them lacked any previous pathway annotations.
dc.description.sponsorshipIB, RH and VS were supported by funding provided by the King Abdullah University of Science and Technology.
dc.language.isoen
dc.publisherPublic Library of Science (PLoS)
dc.relation.urlhttp://dx.plos.org/10.1371/journal.pone.0158896
dc.rightsThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. http://creativecommons.org/licenses/by/4.0/
dc.titlePrediction of Metabolic Pathway Involvement in Prokaryotic UniProtKB Data by Association Rule Mining
dc.typeArticle
dc.contributor.departmentBio-Ontology Research Group (BORG)
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalPLoS ONE
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom
dc.contributor.institutionSoftberry Inc., 116 Radio Circle, Suite 400, Mount Kisco, NY 10549, United States of America
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personBoudellioua, Imene
kaust.personSaidi, Rabie
kaust.personHoehndorf, Robert
refterms.dateFOA2018-06-13T13:15:38Z


Files in this item

Thumbnail
Name:
journal.pone.0158896.PDF
Size:
589.8Kb
Format:
PDF
Description:
Main article

This item appears in the following Collection(s)

Show simple item record