COMPARING MATHEMATICAL METHODS TO DETECT HIV RESISTANCE

点击翻译按钮获取中文摘要

DESCRIPTION (provided by applicant): Objective: Mathematical methods such as neural networks and classification trees are being used more frequently in medicine to analyze high dimensional data such as genetic sequences. They have also been applied to the detection of HIV resistance to antiretrovirals by analyzing mutations in the sequences of the target proteins, reverse transcriptase and protease. This study will build accurate classification models to detect HIV resistance, and compare their performance in correctly detecting resistance and in finding meaningful relationships between specific mutation patterns and resistance. Methods: Protease and reverse transcriptase sequences paired with phenotypic resistance data will be taken from the Stanford HIV Sequence Database, a public database of HIV sequences published in the medical literature. Baseline data will be measured, and a systematic review will be performed to describe any variability between studies. Models will be trained to classify sequences as resistant or susceptible to a specific antiretroviral using logistic regression, neural networks, and classification trees. Meta-modeling will be done to explore sources of heterogeneity. The ability of these models to detect resistance from genotype will be compared to each other using measures of bias, discrimination, and calibration. Resistance mutations and mutation patterns found by the models will be compared to what is already known and well accepted in the literature.