ArticleBioData mining2016
Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification.
Article in BioData mining, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning in HIV Care and Antiretroviral Therapy: Systematic Review.Journal of medical Internet research · 2026Pooled it
- 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
- Multi-label classification for multi-drug resistance prediction ofComputational and structural biotechnology journal · 2022Article
- Predicting HIV drug resistance using weighted machine learning method at target protein sequence-level.Molecular diversity · 2021Article
- Using machine learning and big data to explore the drug resistance landscape in HIV.PLoS computational biology · 2021Article
- Drug Design Strategies to Avoid Resistance in Direct-Acting Antivirals and Beyond.Chemical reviews · 2021Review
- A Computational Approach for the Prediction of Treatment History and the Effectiveness or Failure of Antiretroviral Therapy.International journal of molecular sciences · 2020Article
- DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis.Bioinformatics (Oxford, England) · 2019Article
- HIV drug resistance prediction with weighted categorical kernel functions.BMC bioinformatics · 2019Article
- Fuzzy-multidimensional deep learning for efficient prediction of patient response to antiretroviral therapy.Heliyon · 2019Article
- Genomic prediction of tuberculosis drug-resistance: benchmarking existing databases and prediction algorithms.BMC bioinformatics · 2019Article
- STS-NLSP: A Network-Based Label Space Partition Method for Predicting the Specificity of Membrane Transporter Substrates Using a Hybrid Feature of Structural and Semantic Similarity.Frontiers in bioengineering and biotechnology · 2019Article
- A Computational Approach for the Prediction of HIV Resistance Based on Amino Acid and Nucleotide Descriptors.Molecules (Basel, Switzerland) · 2018Article
- Molecular Antiretroviral Resistance Markers of Human Immunodeficiency Virus-1 of CRF01_AE Subtype in Bali, Indonesia.Current HIV research · 2018Observational
- Decoding HIV resistance: from genotype to therapy.Future medicinal chemistry · 2017Review
- eccCL: parallelized GPU implementation of Ensemble Classifier Chains.BMC bioinformatics · 2017Article
- ACE: an efficient and sensitive tool to detect insecticide resistance-associated mutations in insect acetylcholinesterase from RNA-Seq data.BMC bioinformatics · 2017Article
- Preservation affinity in consensus modules among stages of HIV-1 progression.BMC bioinformatics · 2017Article
- Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach.BioData mining · 2016Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAntiretroviral therapy is essential for human immunodeficiency virus (HIV) infected patients to inhibit viral replication and therewith to slow progression of disease and prolong a patient's life. However, the high mutation rate of HIV can lead to a fast adaptation of the virus under drug pressure and thereby to the evolution of resistant variants. In turn, these variants will lead to the failure of antiretroviral treatment. Moreover, these mutations cannot only lead to resistance against single drugs, but also to cross-resistance, i.e., resistance against drugs that have not yet been applied.
methods662 protease sequences and 715 reverse transcriptase sequences with complete resistance profiles were analyzed using machine learning techniques, namely binary relevance classifiers, classifier chains, and ensembles of classifier chains.
resultsIn our study, we applied multi-label classification models incorporating cross-resistance information to predict drug resistance for two of the major drug classes used in antiretroviral therapy for HIV-1, namely protease inhibitors (PIs) and non-nucleoside reverse transcriptase inhibitors (NNRTIs). By means of multi-label learning, namely classifier chains (CCs) and ensembles of classifier chains (ECCs), we were able to improve overall prediction accuracy for all drugs compared to hitherto applied binary classification models.
conclusionsThe development of fast and precise models to predict drug resistance in HIV-1 is highly important to enable a highly effective personalized therapy. Cross-resistance information can be exploited to improve prediction accuracy of computational drug resistance models.
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