Rectified Classifier Chains for Prediction of Antibiotic Resistance from Multi-Labelled Data with Missing Labels

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dc.contributor.authorMukunthan, T.
dc.contributor.authorBrian, G.
dc.contributor.authorRoberto La, R.
dc.contributor.authorAnil, F.
dc.date.accessioned2023-12-28T06:23:28Z
dc.date.available2023-12-28T06:23:28Z
dc.date.issued2023
dc.description.abstractPredictingAntimicrobialResistance(AMR)fromgenomicdatahasimportantimplicationsforhumanandanimalhealthcare, andespeciallygivenitspotentialformorerapiddiagnosticsandinformedtreatmentchoices.Withtherecentadvancesinsequencing technologies,applyingmachinelearningtechniquesforAMRpredictionhaveindicatedpromisingresults.Despitethis,thereare shortcomingsintheliteratureconcerningmethodologiessuitableformulti-drugAMRpredictionandespeciallywheresampleswith missinglabelsexist.Toaddressthisshortcoming,weintroduceaRectifiedClassifierChain(RCC)methodforpredictingmulti-drug resistance.ThisRCCmethodwastestedusingannotatedfeaturesofgenomicssequencesandcomparedwithsimilarmulti-label classificationmethodologies.WefoundthatapplyingtheeXtremeGradientBoosting(XGBoost)basemodeltoourRCCmodel outperformedthesecond-bestmodel,XGBoostbasedbinaryrelevancemodel,by3.3%inHammingaccuracyand7.8%inF1-score. Additionally,wenotethatintheliteraturemachinelearningmodelsappliedtoAMRpredictiontypicallyareunsuitableforidentifying biomarkersinformativeoftheirdecisions;inthisstudy,weshowthatbiomarkerscontributingtoAMRpredictioncanalsobeidentified usingtheproposedRCCmethod.Weexpectthiscanfacilitategenomeannotationandpavethepathtowardsidentifyingnew biomarkersindicativeofAMR.en_US
dc.identifier.urihttp://repo.lib.jfn.ac.lk/ujrr/handle/123456789/10003
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectMulti-label classificationen_US
dc.subjectClassifier chain,en_US
dc.subjectMulti-drug AMRen_US
dc.subjectMissing labelsen_US
dc.subjectSemi-supervised modelen_US
dc.subjectFeature selectionen_US
dc.titleRectified Classifier Chains for Prediction of Antibiotic Resistance from Multi-Labelled Data with Missing Labelsen_US
dc.typeArticleen_US

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