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HIV drug resistance prediction with categorical kernel functions

Author
Ramon, E.; Pérez, M.; Belanche, Ll.
Type of activity
Presentation of work at congresses
Name of edition
7th International Work-Conference on Bioinformatics and Biomedical Engineering
Date of publication
2019
Presentation's date
2019-05
Book of congress proceedings
Bioinformatics and Biomedical Engineering, 7th International Work-Conference, IWBBIO 2019: Granada, Spain, May 8-10, 2019: proceedings, part II
DOI
10.1007/978-3-030-17935-9_22
URL
https://link.springer.com/chapter/10.1007/978-3-030-17935-9_22 Open in new window
Abstract
Antiretroviral drugs are a very effective therapy against HIV infection. However, the high mutation rate of HIV permits the emergence of variants that can be resistant to the drug treatment. In this paper, we propose the use of categorical kernel functions to predict the resistance to 18 drugs from virus sequence data. These kernel functions are able to take into account HIV data particularities, as are the allele mixtures, and to integrate additional knowledge about the major resistance associa...
Keywords
Categorical kernel, Drug resistance prediction, HIV, SVMs
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
SOCO - Soft Computing

Participants

  • Ramon Gurrea, Elies  (author and speaker )
  • Pérez Enciso, Miguel  (author and speaker )
  • Belanche Muñoz, Luis Antonio  (author and speaker )