Sequence-based predictive models of resistance to HIV-1 integrase inhibitors: An n-grams approach to phenotype assessment

Current HIV Research

Volumn 13, Issue 6, 2015, Pages 497-502

  Masso, Majid ^a  

^a GEORGE MASON UNIVERSITY   (United States)

Author keywords

Drug resistance; Genotype phenotype correlations; HIV 1 integrase; N grams; Regression; Statistical learning models; Supervised classification

Indexed keywords

ELVITEGRAVIR; INTEGRASE INHIBITOR; RALTEGRAVIR; RNA DIRECTED DNA POLYMERASE INHIBITOR; VIRUS DNA; INTEGRASE; P31 INTEGRASE PROTEIN, HUMAN IMMUNODEFICIENCY VIRUS 1;

AMINO ACID SEQUENCE; ARTICLE; CONTROLLED STUDY; DECISION MAKING; DRUG RESISTANCE; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; PHENOTYPE; SENSITIVITY AND SPECIFICITY; VIRUS REPLICATION; VIRUS RESISTANCE; AMINO ACID SUBSTITUTION; ANTIVIRAL RESISTANCE; BIOSTATISTICS; DRUG EFFECTS; ENZYMOLOGY; GENETICS; GENOTYPE; GENOTYPING TECHNIQUE; HUMAN IMMUNODEFICIENCY VIRUS 1; MACHINE LEARNING; MICROBIAL SENSITIVITY TEST; PROCEDURES;

AMINO ACID SUBSTITUTION; BIOSTATISTICS; DRUG RESISTANCE, VIRAL; GENOTYPE; GENOTYPING TECHNIQUES; HIV INTEGRASE; HIV INTEGRASE INHIBITORS; HIV-1; HUMANS; MACHINE LEARNING; MICROBIAL SENSITIVITY TESTS; PHENOTYPE;

EID: 84940912727     PISSN: 1570162X     EISSN: 18734251     Source Type: Journal    
DOI: 10.2174/1570162X13666150624100535     Document Type: Article

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