Please use this identifier to cite or link to this item: http://repository.iiitd.edu.in/xmlui/handle/123456789/1474
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSista, Anurag
dc.contributor.authorSengupta, Debarka (Advisor)
dc.date.accessioned2024-05-16T08:18:03Z
dc.date.available2024-05-16T08:18:03Z
dc.date.issued2023-11-29
dc.identifier.urihttp://repository.iiitd.edu.in/xmlui/handle/123456789/1474
dc.description.abstractThis study addresses the critical issue of antimicrobial resistance (AMR) in Escherichia coli through machine learning techniques. Our methodology includes using already available E.coli strains that are susceptible and resistant to antibiotics and implementing machine learning models on this data . The goal is to develop a machine learning model for predicting anitmicrobial resistance.en_US
dc.language.isoen_USen_US
dc.publisherIIIT-Delhien_US
dc.subjectMachine Learningen_US
dc.subjectEmbedding Sequencesen_US
dc.titlePrediction of antimicrobial resistance in E.coli based on genome sequenceen_US
dc.typeOtheren_US
Appears in Collections:Year-2023

Files in This Item:
File Description SizeFormat 
btp_report_2020495 - Anurag Sista.pdf
  Restricted Access
281.43 kBAdobe PDFView/Open Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.