Rectified Classifier Chains for Prediction of Antibiotic Resistance from Multi-Labelled Data with Missing Labels - Mukunthan, T.; Brian, G.; Roberto La, R.; Anil, F. (IEEE, 2023)
PredictingAntimicrobialResistance(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.
Keywords: Multi-label classification; Classifier chain; Multi-drug AMR; Missing labels; Semi-supervised model; Feature selection