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.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Medical Informatics Fellowships (F37)
Project #
5F37LM008028-02
Application #
6810115
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Florance, Valerie
Project Start
2003-09-15
Project End
2005-09-14
Budget Start
2004-09-15
Budget End
2005-09-14
Support Year
2
Fiscal Year
2004
Total Cost
$80,445
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115