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dc.contributor.authorÖzdilek, A. Samet
dc.contributor.authorAtakan, Ahmet
dc.contributor.authorÖzsarı, Gökhan
dc.contributor.authorAcar, Aybar
dc.contributor.authorAtalay, M. Volkan
dc.contributor.authorDoğan, Tunca
dc.contributor.authorRifaioğlu, Ahmet Süreyya
dc.date.accessioned2023-12-21T10:38:25Z
dc.date.available2023-12-21T10:38:25Z
dc.date.issued2023en_US
dc.identifier.citationÖzdilek, A. S., Atakan, A., Özsarı, G., Acar, A., Atalay, M. V., Doğan, T., & Rifaioğlu, A. S. (2023). ProFAB-open protein functional annotation benchmark. Briefings in bioinformatics, 24(2), bbac627. https://doi.org/10.1093/bib/bbac627en_US
dc.identifier.issn1467-5463
dc.identifier.issn1477-4054
dc.identifier.urihttps://doi.org/10.1093/bib/bbac627
dc.identifier.urihttps://hdl.handle.net/20.500.12508/2730
dc.description.abstractAs the number of protein sequences increases in biological databases, computational methods are required to provide accurate functional annotation with high coverage. Although several machine learning methods have been proposed for this purpose, there are still two main issues: (i) construction of reliable positive and negative training and validation datasets, and (ii) fair evaluation of their performances based on predefined experimental settings. To address these issues, we have developed ProFAB: Open Protein Functional Annotation Benchmark, which is a platform providing an infrastructure for a fair comparison of protein function prediction methods. ProFAB provides filtered and preprocessed protein annotation datasets and enables the training and evaluation of function prediction methods via several options. We believe that ProFAB will be useful for both computational and experimental researchers by enabling the utilization of ready-to-use datasets and machine learning algorithms for protein function prediction based on Gene Ontology terms and Enzyme Commission numbers. ProFAB is available at and .en_US
dc.language.isoengen_US
dc.publisherOxford University Pressen_US
dc.relation.isversionof10.1093/bib/bbac627en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject.classificationBiomedical
dc.subject.classificationCuration
dc.subject.classificationMedline
dc.subject.classificationClinical & Life Sciences - Molecular & Cell Biology - Genetics - Gene Expression Data
dc.subject.otherAlgorithms
dc.subject.otherBenchmarking
dc.subject.otherComputational biology
dc.subject.otherMolecular sequence annotation
dc.subject.otherProteins
dc.subject.otherSoftware
dc.subject.otherGene ontology
dc.subject.otherIntermethod comparison
dc.subject.otherMachine learning
dc.subject.otherPrediction
dc.subject.otherProtein function
dc.subject.otherAlgorithm
dc.subject.otherBenchmarking
dc.subject.otherBiology
dc.subject.otherMetabolism
dc.subject.otherMolecular genetics
dc.subject.otherProcedures
dc.subject.otherSoftware
dc.titleProFAB—open protein functional annotation benchmarken_US
dc.typearticleen_US
dc.relation.journalBriefings in Bioinformaticsen_US
dc.contributor.departmentMühendislik ve Doğa Bilimleri Fakültesi -- Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.identifier.volume24en_US
dc.identifier.issue2en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.isteauthorRifaioğlu, Ahmet Süreyya
dc.relation.indexWeb of Science - Scopus - PubMeden_US
dc.relation.indexWeb of Science Core Collection - Science Citation Index Expanded


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