SLPred: a multi-view subcellular localization prediction tool for multi-location human proteins

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dc.authoridDogan, Tunca/0000-0002-1298-9763
dc.authoridMartin, Maria-Jesus/0000-0001-5454-2815
dc.authoridCetin-Atalay, Rengul/0000-0003-2408-6606
dc.authoridAtakan, Ahmet/0000-0001-9660-9758
dc.authoridOZSARI, GOKHAN/0000-0002-3023-9843
dc.authoridAtalay, Volkan/0000-0001-7850-0601
dc.contributor.authorOzsari, Gokhan
dc.contributor.authorRifaioglu, Ahmet Sureyya
dc.contributor.authorAtakan, Ahmet
dc.contributor.authorTunca Dogan
dc.contributor.authorMartin, Maria Jesus
dc.contributor.authorAtalay, Rengul Cetin
dc.contributor.authorAtalay, Volkan
dc.date.accessioned2024-11-07T13:35:26Z
dc.date.available2024-11-07T13:35:26Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractAccurate prediction of the subcellular locations (SLs) of proteins is a critical topic in protein science. In this study, we present SLPred, an ensemble-based multi-view and multi-label protein subcellular localization prediction tool. For a query protein sequence, SLPred provides predictions for nine main SLs using independent machine-learning models trained for each location. We used UniProtKB/Swiss-Prot human protein entries and their curated SL annotations as our source data. We connected all disjoint terms in the UniProt SL hierarchy based on the corresponding term relationships in the cellular component category of Gene Ontology and constructed a training dataset that is both reliable and large scale using the re-organized hierarchy. We tested SLPred on multiple benchmarking datasets including our-in house sets and compared its performance against six state-of-the-art methods. Results indicated that SLPred outperforms other tools in the majority of cases.
dc.identifier.doi10.1093/bioinformatics/btac458
dc.identifier.endpage4229
dc.identifier.issn1367-4803
dc.identifier.issn1367-4811
dc.identifier.issue17
dc.identifier.pmid35801913
dc.identifier.scopus2-s2.0-85141891019
dc.identifier.scopusqualityQ1
dc.identifier.startpage4226
dc.identifier.urihttps://doi.org/10.1093/bioinformatics/btac458
dc.identifier.urihttps://hdl.handle.net/11480/16499
dc.identifier.volume38
dc.identifier.wosWOS:000823467600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherOxford Univ Press
dc.relation.ispartofBioinformatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectOntology
dc.subjectSequence
dc.titleSLPred: a multi-view subcellular localization prediction tool for multi-location human proteins
dc.typeArticle

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