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Drug resistance classification for Mycobacterium tuberculosis using multi-output model with stacked auto-encoders

Abstract:
This work explores deep-learning model to classify drug resistance for Mycobacterium tuberculosis. We applied an end-to-end model on DNA mutations of the pathogen and lab-based phenotyping results. The model first stacks 3 auto-encoders, and then applies multiple classifiers to classify resistance for four first-line drugs. The results is promising and show the potential of the model for drug resistance analysis.
Publication status:
In press
Peer review status:
Reviewed (other)

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Funding agency for:
Yang, Y
Grant:
KC Wong Fellowship
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Funding agency for:
Clifton, D
Grant:
“Gr
Challenge”'award
Bill and Melinda Gates Foundation More from this funder
Wellcome Trust More from this funder
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Funding agency for:
Clifton, D
Grant:
“Gr
Challenge”'award
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17) Journal website
Host title:
39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17)
Publication date:
2017-05-01
Acceptance date:
2017-05-16
Source identifiers:
697679
Pubs id:
pubs:697679
UUID:
uuid:13b2e972-d194-4a31-9831-76696291bf27
Local pid:
pubs:697679
Deposit date:
2017-05-30

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