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HIV drug resistance prediction with weighted categorical kernel functions

Author
Ramon, E.; Belanche, Ll.; Pérez, M.
Type of activity
Journal article
Journal
BMC bioinformatics
Date of publication
2019-07-30
Volume
20
Number
Article 410
First page
1
Last page
13
DOI
10.1186/s12859-019-2991-2
Repository
http://hdl.handle.net/2117/168438 Open in new window
URL
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2991-2 Open in new window
Abstract
Background 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. Predicting drug resistance to previously unobserved variants is therefore very important for an optimum medical treatment. In this paper, we propose the use of weighted categorical kernel functions to predict drug resistance from virus sequence data. These kernel functions are very simple to imple...
Citation
Ramón, E.; Belanche, L.; Pérez, M. HIV drug resistance prediction with weighted categorical kernel functions. "BMC bioinformatics", 30 Juliol 2019, vol. 20, article 410, p. 1-13.
Keywords
Categorical kernel, Drug resistance prediction, HIV, INI, Kernel PCA, Machine learning, NNRTI, NRTI, PI, Random Forest, Support vector machine, Weighted kernel
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
SOCO - Soft Computing

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