Bioinformatics approach to predicting HIV drug resistance

Expert Review of Molecular Diagnostics

Volumn 6, Issue 2, 2006, Pages 207-215

  Cordes, Frank ^b   Kaiser, Rolf ^c   Selbig, Joachim ^a  

^a UNIVERSITY OF POTSDAM   (Germany)

^b ZUSE INSTITUTE BERLIN   (Germany)

^c UNIVERSITY OF COLOGNE   (Germany)

Author keywords

Bioinformatics; Clustering; Drug resistance; Genotype; HIV 1; Machine learning; Phenotype

Indexed keywords

ABACAVIR; ABACAVIR PLUS LAMIVUDINE PLUS ZIDOVUDINE; AMPRENAVIR; ATAZANAVIR; DIDANOSINE; LAMIVUDINE; LOPINAVIR PLUS RITONAVIR; STAVUDINE; ZALCITABINE; ZIDOVUDINE; ANTI HUMAN IMMUNODEFICIENCY VIRUS AGENT;

BIOINFORMATICS; CELL CULTURE; DRUG CONFORMATION; DRUG RESISTANCE; DRUG SENSITIVITY; GENETIC VARIABILITY; GENOTYPE; HIGHLY ACTIVE ANTIRETROVIRAL THERAPY; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS 1; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; IMMUNE SYSTEM; PHENOTYPE; REVIEW; VIRAL GENETICS; VIRUS MUTATION; VIRUS REPLICATION; ANTIBIOTIC RESISTANCE; BIOLOGY; CHEMICAL STRUCTURE; COMPUTER PROGRAM; COST BENEFIT ANALYSIS; METHODOLOGY; MUTATION; PHARMACOGENETICS; PROTEIN CONFORMATION; STRUCTURE ACTIVITY RELATION;

ANTI-HIV AGENTS; COMPUTATIONAL BIOLOGY; COST-BENEFIT ANALYSIS; DRUG RESISTANCE, VIRAL; GENOTYPE; HIV INFECTIONS; HUMANS; MODELS, MOLECULAR; MUTATION; PHARMACOGENETICS; PHENOTYPE; PROTEIN CONFORMATION; SOFTWARE; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 33744913896     PISSN: 14737159     EISSN: 17448352     Source Type: Journal    
DOI: 10.1586/14737159.6.2.207     Document Type: Review

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