HIV drug resistance prediction with weighted categorical kernel functions

BMC Bioinformatics
Elies RamonMiguel Pérez-Enciso

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. 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. We analyzed 21 drugs of four classes: protease inhibitors (PI), integrase inhibitors (INI), nucleoside reverse transcriptase inhibitors (NRTI) and non-nucleoside reverse transcriptase inhibitors (NNRTI). We compared two categorical kernel functions, Overlap and Jaccard, against two well-known noncategorical kernel functions (Linear and RBF) and Random Forest (RF). Weighted versions of these kernels were also considered, where the weights were obtained from the RF decrease in node impurity. The Jaccard kernel was the best me...Continue Reading

References

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Citations

Jan 26, 2020·International Journal of Molecular Sciences·Olga TarasovaVladimir Poroikov
Dec 31, 2020·BMC Bioinformatics·Dhara ShahRobert W Harrison
Mar 17, 2021·FEMS Microbiology Reviews·Stephen J GoodswenJohn T Ellis
May 6, 2021·Current Medicinal Chemistry·Olga Tarasova, Vladimir Poroikov

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ANRS
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