TransAMR: An Interpretable Transformer Model for Accurate Prediction of Antimicrobial Resistance Using Antibiotic Administration Data-Reference-Cited by-同舟云学术

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TransAMR: An Interpretable Transformer Model for Accurate Prediction of Antimicrobial Resistance Using Antibiotic Administration Data

Published:2023 Issue: Volume:11 Page:75337-75350
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Tharmakulasingam Mukunthan¹^ORCID,Wang Wenwu¹^ORCID,Kerby Michael²,Ragione Roberto La³^ORCID,Fernando Anil⁴

Affiliation:

1. Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, U.K.

2. Synergy Farm Health Ltd., Maiden Newton, Dorset, U.K.

3. School of Veterinary Medicine, University of Surrey, Guildford, U.K.

4. Department of Computer and Information Sciences, University of Strathclyde, Glasgow, U.K.

Funder

University of Surrey

Zoetis through the vHive Initiative

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10185002.pdf?arnumber=10185002

Reference35 articles.

1. Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons

2. Deep Learning With TensorFlow: A Review

3. Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN

4. Scikit-learn: Machine learning in Python;pedregosa;J Mach Learn Res,2012

5. A biochemically-interpretable machine learning classifier for microbial GWAS

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Automated extraction of standardized antibiotic resistance and prescription data from laboratory information systems and electronic health records: a narrative review;Frontiers in Antibiotics;2024-03-08

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