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

Alexander G. Holman; Dana Gabuzda

doi:10.1371/journal.pone.0049538

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Open Access

Peer-reviewed

Research Article

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

Alexander G. Holman,

Affiliation Department of Cancer Immunology and AIDS, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
⨯
Dana Gabuzda

* E-mail: Dana_Gabuzda@dfci.harvard.edu

Affiliations Department of Cancer Immunology and AIDS, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America, Department of Neurology (Microbiology, and Immunobiology), Harvard Medical School, Boston, Massachusetts, United States of America
⨯

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

Alexander G. Holman,
Dana Gabuzda

Published: November 14, 2012
https://doi.org/10.1371/journal.pone.0049538

About the Authors

Alexander G. Holman

Affiliation Department of Cancer Immunology and AIDS, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America

Dana Gabuzda

* E-mail: Dana_Gabuzda@dfci.harvard.edu

Affiliations Department of Cancer Immunology and AIDS, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America, Department of Neurology (Microbiology, and Immunobiology), Harvard Medical School, Boston, Massachusetts, United States of America

Competing Interests

The authors have declared that no competing interests exist.

Author Contributions

Conceived and designed the experiments: AGH DG. Performed the experiments: AGH. Analyzed the data: AGH DG. Wrote the paper: AGH DG.

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HIV
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Amino acid analysis
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Protein sequencing
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Machine learning algorithms
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Cerebrospinal fluid
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Viral replication
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Antiretroviral therapy
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Machine learning
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