Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations

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dc.contributor.authorBi, Y.
dc.contributor.authorLi, F.
dc.contributor.authorGuo, X.
dc.contributor.authorWang, Z.
dc.contributor.authorPan, T.
dc.contributor.authorGuo, Y.
dc.contributor.authorWebb, G.I.
dc.contributor.authorYao, J.
dc.contributor.authorJia, C.
dc.contributor.authorSong, J.
dc.date.issued2022
dc.description.abstractSubcellular localization of messenger RNAs (mRNAs) plays a key role in the spatial regulation of gene activity. The functions of mRNAs have been shown to be closely linked with their localizations. As such, understanding of the subcellular localizations of mRNAs can help elucidate gene regulatory networks. Despite several computational methods that have been developed to predict mRNA localizations within cells, there is still much room for improvement in predictive performance, especially for the multiple-location prediction. In this study, we proposed a novel multi-label multi-class predictor, termed Clarion, for mRNA subcellular localization prediction. Clarion was developed based on a manually curated benchmark dataset and leveraged the weighted series method for multi-label transformation. Extensive benchmarking tests demonstrated Clarion achieved competitive predictive performance and the weighted series method plays a crucial role in securing superior performance of Clarion. In addition, the independent test results indicate that Clarion outperformed the state-of-the-art methods and can secure accuracy of 81.47, 91.29, 79.77, 92.10, 89.15, 83.74, 80.74, 79.23 and 84.74% for chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus and ribosome, respectively. The webserver and local stand-alone tool of Clarion is freely available at http://monash.bioweb.cloud.edu.au/Clarion/.
dc.description.statementofresponsibilityYue Bi, Fuyi Li, Xudong Guo, Zhikang Wang, Tong Pan, Yuming Guo, Geoffrey I. Webb, Jianhua Yao, Cangzhi Jia and Jiangning Song
dc.identifier.citationBriefings in Bioinformatics, 2022; 23(6):bbac467-1-bbac467-12
dc.identifier.doi10.1093/bib/bbac467
dc.identifier.issn1467-5463
dc.identifier.issn1477-4054
dc.identifier.orcidLi, F. [0000-0001-5216-3213]
dc.identifier.urihttps://hdl.handle.net/2440/139562
dc.language.isoen
dc.publisherOxford University Press (OUP)
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1127948
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1144652
dc.relation.granthttp://purl.org/au-research/grants/arc/LP110200333
dc.relation.granthttp://purl.org/au-research/grants/arc/DP120104460
dc.rights© The Author(s) 2022. Published by Oxford University Press. All rights reserved.
dc.source.urihttps://doi.org/10.1093/bib/bbac467
dc.subjectmRNA; subcellular localization; sequence analysis; machine learning; multi-class classification; multi-label prediction
dc.subject.meshCell Nucleus
dc.subject.meshProteins
dc.subject.meshRNA, Messenger
dc.subject.meshComputational Biology
dc.subject.meshDatabases, Protein
dc.titleClarion is a multi-label problem transformation method for identifying mRNA subcellular localizations
dc.typeJournal article
pubs.publication-statusPublished

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