HIV drug resistance prediction with weighted categorical kernel functions.

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 implement and are able to take into account HIV data particularities, such as allele mixtures, and to weigh the different importance of each protein residue, as it is known that not all positions contribute equally to the resistance.

Results

We analyzed 21 drugs of four classes: protease i

SUBMITTER: Ramon E 

PROVIDER: S-EPMC6668108 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature