Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144754
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dc.contributor.authorZhao, Yaxingen_US
dc.contributor.authorSue, Andrew Chi-Hauen_US
dc.contributor.authorGoh, Wilson Wen Binen_US
dc.date.accessioned2020-11-23T07:22:34Z-
dc.date.available2020-11-23T07:22:34Z-
dc.date.issued2019-
dc.identifier.citationZhao, Y., Sue, A. C.-H., & Goh, W. W. B. (2019). Deeper investigation into the utility of functional class scoring in missing protein prediction from proteomics data. Journal of Bioinformatics and Computational Biology, 17(2), 1950013-. doi:10.1142/S0219720019500136en_US
dc.identifier.issn0219-7200en_US
dc.identifier.urihttps://hdl.handle.net/10356/144754-
dc.description.abstractFunctional Class Scoring (FCS) is a network-based approach previously demonstrated to be powerful in missing protein prediction (MPP). We update its performance evaluation using data derived from new proteomics technology (SWATH) and also checked for reproducibility using two independent datasets profiling kidney tissue proteome. We also evaluated the objectivity of the FCS p-value, and followed up on the value of MPP from predicted complexes. Our results suggest that (1) FCS p -values are non-objective, and are confounded strongly by complex size, (2) best recovery performance do not necessarily lie at standard p -value cutoffs, (3) while predicted complexes may be used for augmenting MPP, they are inferior to real complexes, and are further confounded by issues relating to network coverage and quality and (4) moderate sized complexes of size 5 to 10 still exhibit considerable instability, we find that FCS works best with big complexes. While FCS is a powerful approach, blind reliance on its non-objective p -value is ill-advised.en_US
dc.language.isoenen_US
dc.relation.ispartofJournal of Bioinformatics and Computational Biologyen_US
dc.rights© 2019 World Scientific Publishing Europe Ltd. All rights reserved.en_US
dc.subjectScience::Biological sciencesen_US
dc.titleDeeper investigation into the utility of functional class scoring in missing protein prediction from proteomics dataen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Biological Sciencesen_US
dc.identifier.doi10.1142/S0219720019500136-
dc.identifier.pmid31057071-
dc.identifier.issue2en_US
dc.identifier.volume17en_US
dc.subject.keywordsProteomicsen_US
dc.subject.keywordsFunctional Class Scoringen_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
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