Literature DB >> 16980939

Bioinformatics-assisted anti-HIV therapy.

Thomas Lengauer1, Tobias Sing.   

Abstract

Highly active antiretroviral therapy (HAART), in which three or more drugs are given in combination, has substantially improved the clinical management of HIV-1 infection. Still, the emergence of drug-resistant variants eventually leads to therapy failure in most patients. In such a scenario, the high diversity of resistance-associated mutational patterns complicates the choice of an optimal follow-up regimen. To support physicians in this task, a range of bioinformatics tools for predicting drug resistance or response to combination therapy from the viral genotype have been developed. With several free and commercial software services available, computational advice is rapidly gaining acceptance as an important element of rational decision-making in the treatment of HIV infection.

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Year:  2006        PMID: 16980939     DOI: 10.1038/nrmicro1477

Source DB:  PubMed          Journal:  Nat Rev Microbiol        ISSN: 1740-1526            Impact factor:   60.633


  26 in total

1.  Detecting and understanding combinatorial mutation patterns responsible for HIV drug resistance.

Authors:  Jing Zhang; Tingjun Hou; Wei Wang; Jun S Liu
Journal:  Proc Natl Acad Sci U S A       Date:  2010-01-11       Impact factor: 11.205

2.  A phylogenetic and Markov model approach for the reconstruction of mutational pathways of drug resistance.

Authors:  Patricia Buendia; Brice Cadwallader; Victor DeGruttola
Journal:  Bioinformatics       Date:  2009-08-03       Impact factor: 6.937

3.  Extracting causal relations on HIV drug resistance from literature.

Authors:  Quoc-Chinh Bui; Breanndán O Nualláin; Charles A Boucher; Peter M A Sloot
Journal:  BMC Bioinformatics       Date:  2010-02-23       Impact factor: 3.169

Review 4.  Managing drug resistance in cancer: lessons from HIV therapy.

Authors:  Christoph Bock; Thomas Lengauer
Journal:  Nat Rev Cancer       Date:  2012-06-07       Impact factor: 60.716

5.  Structure of the antiviral assembly inhibitor CAP-1 complex with the HIV-1 CA protein.

Authors:  Brian N Kelly; Sampson Kyere; Isaac Kinde; Chun Tang; Bruce R Howard; Howard Robinson; Wesley I Sundquist; Michael F Summers; Christopher P Hill
Journal:  J Mol Biol       Date:  2007-08-15       Impact factor: 5.469

6.  geno2pheno[ngs-freq]: a genotypic interpretation system for identifying viral drug resistance using next-generation sequencing data.

Authors:  Matthias Döring; Joachim Büch; Georg Friedrich; Alejandro Pironti; Prabhav Kalaghatgi; Elena Knops; Eva Heger; Martin Obermeier; Martin Däumer; Alexander Thielen; Rolf Kaiser; Thomas Lengauer; Nico Pfeifer
Journal:  Nucleic Acids Res       Date:  2018-07-02       Impact factor: 16.971

7.  A multifaceted analysis of HIV-1 protease multidrug resistance phenotypes.

Authors:  Kathleen M Doherty; Priyanka Nakka; Bracken M King; Soo-Yon Rhee; Susan P Holmes; Robert W Shafer; Mala L Radhakrishnan
Journal:  BMC Bioinformatics       Date:  2011-12-15       Impact factor: 3.169

8.  Towards bioinformatics assisted infectious disease control.

Authors:  Vitali Sintchenko; Blanca Gallego; Grace Chung; Enrico Coiera
Journal:  BMC Bioinformatics       Date:  2009-02-05       Impact factor: 3.169

9.  The individualized genetic barrier predicts treatment response in a large cohort of HIV-1 infected patients.

Authors:  Niko Beerenwinkel; Hesam Montazeri; Heike Schuhmacher; Patrick Knupfer; Viktor von Wyl; Hansjakob Furrer; Manuel Battegay; Bernard Hirschel; Matthias Cavassini; Pietro Vernazza; Enos Bernasconi; Sabine Yerly; Jürg Böni; Thomas Klimkait; Cristina Cellerai; Huldrych F Günthard
Journal:  PLoS Comput Biol       Date:  2013-08-29       Impact factor: 4.475

10.  Comparison of classifier fusion methods for predicting response to anti HIV-1 therapy.

Authors:  André Altmann; Michal Rosen-Zvi; Mattia Prosperi; Ehud Aharoni; Hani Neuvirth; Eugen Schülter; Joachim Büch; Daniel Struck; Yardena Peres; Francesca Incardona; Anders Sönnerborg; Rolf Kaiser; Maurizio Zazzi; Thomas Lengauer
Journal:  PLoS One       Date:  2008-10-21       Impact factor: 3.240

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