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dc.contributor.authorSaidi, Rabie
dc.contributor.authorBoudellioua, Imene
dc.contributor.authorMartin, Maria J.
dc.contributor.authorSolovyev, Victor
dc.date.accessioned2017-10-30T08:39:49Z
dc.date.available2017-10-30T08:39:49Z
dc.date.issued2017-08-29
dc.identifier.citationSaidi R, Boudellioua I, Martin MJ, Solovyev V (2017) Rule Mining Techniques to Predict Prokaryotic Metabolic Pathways. Biological Networks and Pathway Analysis: 311–331. Available: http://dx.doi.org/10.1007/978-1-4939-7027-8_12.
dc.identifier.issn1064-3745
dc.identifier.issn1940-6029
dc.identifier.doi10.1007/978-1-4939-7027-8_12
dc.identifier.urihttp://hdl.handle.net/10754/625999
dc.description.abstractIt is becoming more evident that computational methods are needed for the identification and the mapping of pathways in new genomes. We introduce an automatic annotation system (ARBA4Path Association Rule-Based Annotator for Pathways) that utilizes rule mining techniques to predict metabolic pathways across wide range of prokaryotes. It was demonstrated that specific combinations of protein domains (recorded in our rules) strongly determine pathways in which proteins are involved and thus provide information that let us very accurately assign pathway membership (with precision of 0.999 and recall of 0.966) to proteins of a given prokaryotic taxon. Our system can be used to enhance the quality of automatically generated annotations as well as annotating proteins with unknown function. The prediction models are represented in the form of human-readable rules, and they can be used effectively to add absent pathway information to many proteins in UniProtKB/TrEMBL database.
dc.description.sponsorshipThe second author conducted this work as part of a research internship at the European Bioinformatics Institute, UniProt team. The funding for this internship was provided by King Abdullah University of Science and Technology. The authors would also like to thank UniProt Consortium for their valuable support and feedback on the development of this work.
dc.publisherSpringer Nature
dc.relation.urlhttps://link.springer.com/protocol/10.1007%2F978-1-4939-7027-8_12
dc.subjectAutomatic annotation
dc.subjectFunctional genomics
dc.subjectMachine learning
dc.subjectPathway prediction
dc.subjectProteomics
dc.subjectRule mining
dc.titleRule Mining Techniques to Predict Prokaryotic Metabolic Pathways
dc.typeBook Chapter
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.journalBiological Networks and Pathway Analysis
dc.contributor.institutionEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, CB10 1SD, , , United Kingdom
dc.contributor.institutionSoftberry Inc., 116 Radio Circle, Suite 400, Mount Kisco, NY, 10549, , United States
kaust.personBoudellioua, Imene
kaust.personMartin, Maria J.
dc.date.published-online2017-08-29
dc.date.published-print2017


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