ArticleViruses2020
Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data.
Article in Viruses, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it, 81 citations in OpenAlex.
- Machine Learning in HIV Care and Antiretroviral Therapy: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Artificial intelligence for HIV care: a global systematic review of current studies and emerging trends.Journal of the International AIDS Society · 2025Pooled it
- Advancing Antiviral Design: Integrating Natural Products, Computation and Targeted Delivery.Chemical biology & drug design · 2026Review
- Machine Learning Identifies Potential Accessory Resistance-Associated Mutations in HIV-1 Integrase.Research square · 2026Article
- Is the molecular microenvironment of the latent HIV reservoir predictable using deep learning approaches?Journal of virology · 2026Review
- Protein language model embeddings improve HIV drug resistance prediction: a comprehensive benchmark with attention-based interpretability.Bioinformatics (Oxford, England) · 2026Article
- Predicting the evolutionary and structural compensation in Tat ARM region across HIV-1 groups using machine learning approach.Scientific reports · 2026Article
- Relativistic triangle-curvature computing for federated HIV-1 protein-sequence monitoring.Scientific reports · 2026Article
- A Robust Ensemble Machine Learning Approach for Inhibitor Discovery: Case Study of HIV-1 NNRTI and Validation Using MD Simulation.Chemistry, an Asian journal · 2026Article
- Machine Learning Application in Enhancing HIV Management and Treatment Outcomes: Revolutionizing HIV Infection.Current HIV research · 2026Review
- The Role of Artificial Intelligence in Modern Medicine: Clinical Applications, Economic Implications, and Ethical Considerations.Current pharmaceutical design · 2026Review
- Artificial intelligence in human immunodeficiency virus mutation prediction and drug design: Advancing personalized treatment and prevention.Pharmaceutical science advances · 2025Review
- Benchmarking Machine Learning Models for HIV-1 Protease Inhibitor Resistance Prediction: Impact of Data Set Construction and Feature Representation.Journal of chemical information and modeling · 2025Article
- AI Methods Tailored to Influenza, RSV, HIV, and SARS-CoV-2: A Focused Review.Pathogens (Basel, Switzerland) · 2025Review
- Scalable and robust machine learning framework for HIV classification using clinical and laboratory data.Scientific reports · 2025Article
- Role of Artificial Intelligence and Personalized Medicine in Enhancing HIV Management and Treatment Outcomes.Life (Basel, Switzerland) · 2025Review
- Innovative Diagnostic Approaches and Challenges in the Management of HIV: Bridging Basic Science and Clinical Practice.Life (Basel, Switzerland) · 2025Review
- HIV multidrug class resistance prediction with a time sliding anchor approach.Bioinformatics advances · 2025Article
- AI applications in HIV research: advances and future directions.Frontiers in microbiology · 2025Review
- Sex-specific prediction of cardiogenic shock after acute coronary syndromes: the SEX-SHOCK score.European heart journal · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 HIV drug resistance allows for real-time evaluation of evolutionary mechanisms. Characterizing the biological process of drug resistance is also critically important for sustained effectiveness of ART. Investigating the link between "black box" deep learning methods applied to this problem and evolutionary principles governing drug resistance has been overlooked to date. Here, we utilized publicly available HIV-1 sequence data and drug resistance assay results for 18 ART drugs to evaluate the performance of three architectures (multilayer perceptron, bidirectional recurrent neural network, and convolutional neural network) for drug resistance prediction, jointly with biological analysis. We identified convolutional neural networks as the best performing architecture and displayed a correspondence between the importance of biologically relevant features in the classifier and overall performance. Our results suggest that the high classification performance of deep learning models is indeed dependent on drug resistance mutations (DRMs). These models heavily weighted several features that are not known DRM locations, indicating the utility of model interpretability to address causal relationships in viral genotype-phenotype data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.