A Machine Learning Approach for Identifying Amino Acid Signatures in the HIV Env Gene Predictive of Dementia

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ID: 266998
2012
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Abstract
The 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
Reference Key
holman2012plosa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Alexander G. Holman;Dana Gabuzda;
Journal PloS one
Year 2012
DOI
10.1371/journal.pone.0049538
URL
https://doi.org/10.1371/journal.pone.0049538
Keywords
Machine learning hiv antiretroviral therapy cerebrospinal fluid machine learning algorithms viral replication protein sequencing amino acid analysis hiv cerebrospinal fluid machine learning algorithms sequence alignment viral replication protein sequencing sequence databases amino acid analysis

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