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dc.contributor.authorHolman, Alexander G.
dc.contributor.authorGabuzda, Dana Helga
dc.date.accessioned2013-04-22T17:44:50Z
dc.date.issued2012
dc.identifier.citationHolman, Alexander G., and Dana Gabuzda. 2012. A machine learning approach for identifying amino acid signatures in the HIV env gene predictive of dementia. PLoS ONE 7(11): e49538.en_US
dc.identifier.issn1932-6203en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:10579218
dc.description.abstractThe identification of nucleotide sequence variations in viral pathogens linked to disease and clinical outcomes is important for developing vaccines and therapies. However, identifying these genetic variations in rapidly evolving pathogens adapting to selection pressures unique to each host presents several challenges. Machine learning tools provide new opportunities to address these challenges. In HIV infection, virus replicating within the brain causes HIV-associated dementia (HAD) and milder forms of neurocognitive impairment in 20–30% of patients with unsuppressed viremia. HIV neurotropism is primarily determined by the viral envelope (env) gene. To identify amino acid signatures in the HIV env gene predictive of HAD, we developed a machine learning pipeline using the PART rule-learning algorithm and C4.5 decision tree inducer to train a classifier on a meta-dataset (n = 860 env sequences from 78 patients: 40 HAD, 38 non-HAD). To increase the flexibility and biological relevance of our analysis, we included 4 numeric factors describing amino acid hydrophobicity, polarity, bulkiness, and charge, in addition to amino acid identities. The classifier had 75% predictive accuracy in leave-one-out cross-validation, and identified 5 signatures associated with HAD diagnosis (p<0.05, Fisher’s exact test). These HAD signatures were found in the majority of brain sequences from 8 of 10 HAD patients from an independent cohort. Additionally, 2 HAD signatures were validated against env sequences from CSF of a second independent cohort. This analysis provides insight into viral genetic determinants associated with HAD, and develops novel methods for applying machine learning tools to analyze the genetics of rapidly evolving pathogens.en_US
dc.language.isoen_USen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofdoi:10.1371/journal.pone.0049538en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3498126/pdf/en_US
dash.licenseLAA
dc.subjectBiologyen_US
dc.subjectComputational Biologyen_US
dc.subjectGenomicsen_US
dc.subjectGenome Evolutionen_US
dc.subjectSequence Analysisen_US
dc.subjectMicrobiologyen_US
dc.subjectVirologyen_US
dc.subjectViral Structureen_US
dc.subjectViral Envelopeen_US
dc.subjectViral Transmission and Infectionen_US
dc.subjectNeuroinvasivenessen_US
dc.subjectNeurovirulenceen_US
dc.subjectImmunodeficiency Virusesen_US
dc.subjectViral Evolutionen_US
dc.subjectMedicineen_US
dc.subjectNeurologyen_US
dc.subjectDementiaen_US
dc.titleA Machine Learning Approach for Identifying Amino Acid Signatures in the HIV Env Gene Predictive of Dementiaen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalPLoS ONEen_US
dash.depositing.authorGabuzda, Dana Helga
dc.date.available2013-04-22T17:44:50Z
dc.identifier.doi10.1371/journal.pone.0049538*
dash.contributor.affiliatedGabuzda, Dana


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