Please use this identifier to cite or link to this item:
http://repository.iiitd.edu.in/xmlui/handle/123456789/1474Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Sista, Anurag | |
| dc.contributor.author | Sengupta, Debarka (Advisor) | |
| dc.date.accessioned | 2024-05-16T08:18:03Z | |
| dc.date.available | 2024-05-16T08:18:03Z | |
| dc.date.issued | 2023-11-29 | |
| dc.identifier.uri | http://repository.iiitd.edu.in/xmlui/handle/123456789/1474 | |
| dc.description.abstract | This 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.iso | en_US | en_US |
| dc.publisher | IIIT-Delhi | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Embedding Sequences | en_US |
| dc.title | Prediction of antimicrobial resistance in E.coli based on genome sequence | en_US |
| dc.type | Other | en_US |
| Appears in Collections: | Year-2023 | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| btp_report_2020495 - Anurag Sista.pdf Restricted Access | 281.43 kB | Adobe PDF | View/Open Request a copy |
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