Improvement of HIV-1 coreceptor tropism prediction by employing selected nucleotide positions of the env gene in a Bayesian network classifier | Zendy

Francisco DíezFuertes | Zendy; Elena Delgado | Zendy; Yolanda Vega | Zendy; Aurora Fernández-García | Zendy; María Teresa Cuevas | Zendy; Milagros Pinilla | Zendy; Valentina E. Garcia | Zendy; Lucía Pérez-Álvarez | Zendy; Michael M. Thomson | Zendy

Open Access

Improvement of HIV-1 coreceptor tropism prediction by employing selected nucleotide positions of the env gene in a Bayesian network classifier

Author(s) -

Francisco DíezFuertes,

Elena Delgado,

Yolanda Vega,

Aurora Fernández-García,

María Teresa Cuevas,

Milagros Pinilla,

Valentina E. Garcia,

Lucía Pérez-Álvarez,

Michael M. Thomson

Publication year - 2013

Publication title -

journal of antimicrobial chemotherapy

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.124

H-Index - 194

eISSN - 1460-2091

pISSN - 0305-7453

DOI - 10.1093/jac/dkt077

Subject(s) - tropism , human immunodeficiency virus (hiv) , virology , computational biology , gene , classifier (uml) , biology , genetics , artificial intelligence , computer science , virus

This study aimed to develop a genotypic method to predict HIV-1 coreceptor usage by employing the nucleotide sequence of the env gene in a tree-augmented naive Bayes (TAN) classifier, and to evaluate its accuracy in prediction compared with other available tools.

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