title: Prediction of HIV mutation changes based on treatment history.
abstract: Few studies have investigated sequential HIV-1 mutation changes in the HIV gene in patients changing antiretroviral drugs. We analyze such data from the HIV Drug Resistance Database at Stanford University using three data mining methods: association rule analysis, logistic regression, and classification trees. Although the AUC measures of the overall prediction is not high, these methods can effectively identify strong predictors of mutation change and focus further analysis by domain experts.
authors.affiliation: Stanford Medical Informatics, Stanford, CA, USA.
authors.forename: Ray S
authors.initials: RS
authors.lastname: Lin
authors.forename: Soo-Yon
authors.initials: SY
authors.lastname: Rhee
authors.forename: Robert W
authors.initials: RW
authors.lastname: Shafer
authors.forename: Amar K
authors.initials: AK
authors.lastname: Das
mesh_terms: D044966:Anti-Retroviral Agents / Q000494:pharmacology*; D002965:Classification; D030541:Databases, Genetic; D015497:HIV-1 / Q000235:genetics*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D016015:Logistic Models; D009154:Mutation
chemical_list: D044966:Anti-Retroviral Agents