Please use this identifier to cite or link to this item:
http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/10003
Title: | Rectified Classifier Chains for Prediction of Antibiotic Resistance from Multi-Labelled Data with Missing Labels |
Authors: | Mukunthan, T. Brian, G. Roberto La, R. Anil, F. |
Keywords: | Multi-label classification;Classifier chain,;Multi-drug AMR;Missing labels;Semi-supervised model;Feature selection |
Issue Date: | 2023 |
Publisher: | IEEE |
Abstract: | 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. |
URI: | http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/10003 |
Appears in Collections: | Electrical & Electronic Engineering |
Files in This Item:
File | Description | Size | Format | |
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Rectified_Classifier_Chains_for_Prediction_of_Antibiotic_Resistance_From_Multi-Labelled_Data_With_Missing_Labels.pdf | 1.47 MB | Adobe PDF | View/Open |
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