Semi-supervised learning for improving prediction of HIV drug resistance

Abstract

Resistance testing is an important tool in today's anti-HIV therapy management for improving the success of antiretroviral therapy. Routinely, the genetic sequence of viral target proteins is obtained. These sequences are then inspected for mutations that might confer resistance to antiretroviral drugs. However, interpretation of the genomic data is challenging. In recent years, approaches that employ supervised statistical learning methods were made available to assist the interpretation of the complex genetic information (e.g. geno2pheno and VircoTYPE). However, these methods rely on large amounts of labeled training data, which are expensive and labor-intensive to obtain. This work evaluates the application of semi-supervised learning (SSL) for improving the prediction of resistance from the viral genome.