COMPARING MATHEMATICAL METHODS TO DETECT HIV RESISTANCE

比较检测 HIV 耐药性的数学方法

• 批准号: 6694980
• 负责人: ALEXANDER R MACALALAD
• 金额: $ 7.13万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2005-09-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6694980
• 关键词: RNA directed DNA polymerase; antiAIDS agent; antiviral agents; drug resistance; endopeptidases; gene mutation; genotype; human immunodeficiency virus; informatics; mathematical model; model design /development; molecular biology information system; phenotype; postdoctoral investigator; statistics /biometry; virus classification; virus genetics

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.

描述（由申请人提供）：