https://doi.org/10.7490/f1000research.1118271.1
Poster
NOT PEER REVIEWED
Download
metrics
VIEWS
345
 
downloads
38
CITE
How to cite this poster:
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 MTL-CNN: a highly-sensitive multi-task CNN learning model for taxonomic assignment of human viruses [version 1; not peer reviewed].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)
NOTE: it is important to ensure the information in square brackets after the title is included in this citation.

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

Author Affiliations
  • Metrics
  • 345 Views
  • 38 Downloads
 
Part of the gateway
Browse by related subjects
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[version 1; not peer reviewed]

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
Comments
0 Comments
 
Sign In
If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password.

The email address should be the one you originally registered with F1000.

Email address not valid, please try again

You registered with F1000 via Google, so we cannot reset your password.

To sign in, please click here.

If you still need help with your Google account password, please click here.

You registered with F1000 via Facebook, so we cannot reset your password.

To sign in, please click here.

If you still need help with your Facebook account password, please click here.

Code not correct, please try again
Email us for further assistance.
Server error, please try again.