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Drug Resistance, Viral

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A graph neural network-based model with out-of-distribution robustness for enhancing antiretroviral therapy outcome prediction for HIV-1.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge, especially when the ART includes drugs with limited effectiveness data. This scarcity of data can arise either due to the introduction of a new drug ...

Using machine learning and big data to explore the drug resistance landscape in HIV.

PLoS computational biology
Drug resistance mutations (DRMs) appear in HIV under treatment pressure. DRMs are commonly transmitted to naive patients. The standard approach to reveal new DRMs is to test for significant frequency differences of mutations between treated and naive...

Predicting HIV drug resistance using weighted machine learning method at target protein sequence-level.

Molecular diversity
Acquired immune deficiency syndrome (AIDS) is a fatal disease caused by human immunodeficiency virus (HIV). Although 23 different drugs have been available, the treatment of AIDS remains challenging because the virus mutates very quickly which can le...

Deciphering Complex Mechanisms of Resistance and Loss of Potency through Coupled Molecular Dynamics and Machine Learning.

Journal of chemical theory and computation
Drug resistance threatens many critical therapeutics through mutations in the drug target. The molecular mechanisms by which combinations of mutations, especially those remote from the active site, alter drug binding to confer resistance are poorly u...

Evolution of drug resistance in HIV protease.

BMC bioinformatics
BACKGROUND: Drug resistance is a critical problem limiting effective antiviral therapy for HIV/AIDS. Computational techniques for predicting drug resistance profiles from genomic data can accelerate the appropriate choice of therapy. These techniques...

Use of Dried Plasma Spots for HIV-1 Viral Load Determination and Drug Resistance Genotyping in Mexican Patients.

BioMed research international
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
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
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.

Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data.

Viruses
The fast replication rate and lack of repair mechanisms of human immunodeficiency virus (HIV) contribute to its high mutation frequency, with some mutations resulting in the evolution of resistance to antiretroviral therapies (ART). As such, studying...

Characterizing Protein-Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease.

Journal of chemical theory and computation
Over the past several decades, atomistic simulations of biomolecules, whether carried out using molecular dynamics or Monte Carlo techniques, have provided detailed insights into their function. Comparing the results of such simulations for a few clo...