<p>MTL-CNN: a highly-sensitive multi-task CNN learning model for taxonomic assignment of human viruses</p>

Haoran Ma; Hon Kim Kenneth Ban; Tin Wee Tan

doi:10.7490/f1000research.1118271.1

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Ma H, Ban HKK and Tan TW. MTL-CNN: a highly-sensitive multi-task CNN learning model for taxonomic assignment of human viruses [version 1; not peer reviewed]. F1000Research 2020, 9(ISCB Comm J):1026 (poster) (https://doi.org/10.7490/f1000research.1118271.1)

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MTL-CNN: a highly-sensitive multi-task CNN learning model for taxonomic assignment of human viruses

Haoran Ma, Hon Kim Kenneth Ban¹, Tin Wee Tan¹

Published 24 Aug 2020 (https://doi.org/10.7490/f1000research.1118271.1)

Author Affiliations

¹ National University of Singapore, Singapore

Copyright © 2020 Ma H et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Published 24 Aug 2020

MTL-CNN: a highly-sensitive multi-task CNN learning model for taxonomic assignment of human viruses
[version 1; not peer reviewed]

Haoran Ma, Hon Kim Kenneth Ban¹, Tin Wee Tan¹

Author Affiliations

¹ National University of Singapore, Singapore

Presented at

28th International Conference on Intelligent Systems for Molecular Biology (ISMB) 2020

Abstract

Competing Interests

No competing interests were disclosed

Keywords

convolutional neural network (CNN), multi-task learning, taxonomic assignment, coverage

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