AIMC Topic: Drug Resistance, Viral
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A Machine Learning Approach for Predicting HIV Reverse Transcriptase Mutation Susceptibility of Biologically Active Compounds.
Journal of chemical information and modeling
Jul 17, 2018
HIV resistance emerging against antiretroviral drugs represents a great threat to the continued prolongation of the lifespans of HIV-infected patients. Therefore, methods capable of predicting resistance susceptibility in the development of compounds...
Improving fold resistance prediction of HIV-1 against protease and reverse transcriptase inhibitors using artificial neural networks.
BMC bioinformatics
Aug 15, 2017
BACKGROUND: Drug resistance in HIV treatment is still a worldwide problem. Predicting resistance to antiretrovirals (ARVs) before starting any treatment is important. Prediction accuracy is essential, as low-accuracy predictions increase the risk of ...
Paraconsistents artificial neural networks applied to the study of mutational patterns of the F subtype of the viral strains of HIV-1 to antiretroviral therapy.
Anais da Academia Brasileira de Ciencias
Mar 4, 2016
The high variability of HIV-1 as well as the lack of efficient repair mechanisms during the stages of viral replication, contribute to the rapid emergence of HIV-1 strains resistant to antiretroviral drugs. The selective pressure exerted by the drug ...
Lopinavir Resistance Classification with Imbalanced Data Using Probabilistic Neural Networks.
Journal of medical systems
Jan 6, 2016
Resistance to antiretroviral drugs has been a major obstacle for long-lasting treatment of HIV-infected patients. The development of models to predict drug resistance is recognized as useful for helping the decision of the best therapy for each HIV+ ...
Use of Dried Plasma Spots for HIV-1 Viral Load Determination and Drug Resistance Genotyping in Mexican Patients.
BioMed research international
Dec 8, 2015
Monitoring antiretroviral therapy using measurements of viral load (VL) and the genotyping of resistance mutations is not routinely performed in low- to middle-income countries because of the high costs of the commercial assays that are used. The ana...
Influenza virus genotype to phenotype predictions through machine learning: a systematic review.
Emerging microbes & infections
Dec 1, 2021
BACKGROUND: There is great interest in understanding the viral genomic predictors of phenotypic traits that allow influenza A viruses to adapt to or become more virulent in different hosts. Machine learning techniques have demonstrated promise in add...
[Prevalence of transmitted drug resistance in HIV-infected treatment-naive patients in Chile].
Revista medica de Chile
Nov 1, 2020
BACKGROUND: Transmitted drug resistance (TDR) occurs in patients with HIV infection who are not exposed to antiretroviral drugs but who are infected with a virus with mutations associated with resistance.
Tackling the problem of HIV drug resistance.
Postepy biochemii
Jan 1, 2016
The virally-encoded HIV-1 protease is an effective target for antiviral drugs, however, treatment for HIV infections is limited by the prevalence of drug resistant viral mutants. In this review, we describe our three-pronged approach to analyze and c...
Computer-Aided Optimization of Combined Anti-Retroviral Therapy for HIV: New Drugs, New Drug Targets and Drug Resistance.
Current HIV research
Jan 1, 2016
BACKGROUND: Resistance to antiretroviral drugs is a complex and evolving area with relevant implications in the treatment of human immunodeficiency virus (HIV) infection. Several rules, algorithms and full-fledged computer programs have been develope...
Long-term Virologic Suppression Despite Presence of Resistance-associated Mutations Among Perinatally HIV-infected Youth.
The Pediatric infectious disease journal
Dec 1, 2015
BACKGROUND: There is limited information on long-term consequences of continuing combination antiretroviral therapy (cART) consisting of <3 active drugs in treatment-experienced youth with perinatal HIV (PHIV). This study describes the clinical outco...
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