Conference item
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)
Actions
Authors
Funding
+ K.C. Wong Education Foundation
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Funding agency for:
Clifton, D
Grant:
“Gr
Challenge”'award
Bill and Melinda Gates Foundation
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Wellcome Trust
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+ Engineering and Physical Sciences Research Council
More from this funder
Funding agency for:
Clifton, D
Grant:
“Gr
Challenge”'award
Bibliographic Details
- 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
Item Description
- Pubs id:
-
pubs:697679
- UUID:
-
uuid:13b2e972-d194-4a31-9831-76696291bf27
- Local pid:
- pubs:697679
- Deposit date:
- 2017-05-30
Terms of use
- Copyright date:
- 2017
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