Please use this identifier to cite or link to this item:
http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/10003Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Mukunthan, T. | - |
| dc.contributor.author | Brian, G. | - |
| dc.contributor.author | Roberto La, R. | - |
| dc.contributor.author | Anil, F. | - |
| dc.date.accessioned | 2023-12-28T06:23:28Z | - |
| dc.date.available | 2023-12-28T06:23:28Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/10003 | - |
| dc.description.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. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE | en_US |
| dc.subject | Multi-label classification | en_US |
| dc.subject | Classifier chain, | en_US |
| dc.subject | Multi-drug AMR | en_US |
| dc.subject | Missing labels | en_US |
| dc.subject | Semi-supervised model | en_US |
| dc.subject | Feature selection | en_US |
| dc.title | Rectified Classifier Chains for Prediction of Antibiotic Resistance from Multi-Labelled Data with Missing Labels | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | Electrical & Electronic Engineering | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Rectified_Classifier_Chains_for_Prediction_of_Antibiotic_Resistance_From_Multi-Labelled_Data_With_Missing_Labels.pdf | 1.47 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.