ArticleBioData mining2011
Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers.
Article in BioData mining, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A parametric approach for molecular encodings using multilevel atomic neighborhoods applied to peptide classification.NAR genomics and bioinformatics · 2023Article
- A large-scale comparative study on peptide encodings for biomedical classification.NAR genomics and bioinformatics · 2021Article
- Encodings and models for antimicrobial peptide classification for multi-resistant pathogens.BioData mining · 2019Review
- A Computational Approach for the Prediction of HIV Resistance Based on Amino Acid and Nucleotide Descriptors.Molecules (Basel, Switzerland) · 2018Article
- SHIVA - a web application for drug resistance and tropism testing in HIV.BMC bioinformatics · 2016Article
- Genotypic Prediction of Co-receptor Tropism of HIV-1 Subtypes A and C.Scientific reports · 2016Article
- Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach.BioData mining · 2016Article
- Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification.BioData mining · 2016Article
- MANIA-a pattern classification toolbox for neuroimaging data.Neuroinformatics · 2014Article
- A simple structure-based model for the prediction of HIV-1 co-receptor tropism.BioData mining · 2014Article
- SDS, a structural disruption score for assessment of missense variant deleteriousness.Frontiers in genetics · 2014Article
- Inference of global HIV-1 sequence patterns and preliminary feature analysis.Virologica Sinica · 2013Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMaturation inhibitors such as Bevirimat are a new class of antiretroviral drugs that hamper the cleavage of HIV-1 proteins into their functional active forms. They bind to these preproteins and inhibit their cleavage by the HIV-1 protease, resulting in non-functional virus particles. Nevertheless, there exist mutations in this region leading to resistance against Bevirimat. Highly specific and accurate tools to predict resistance to maturation inhibitors can help to identify patients, who might benefit from the usage of these new drugs.
resultsWe tested several methods to improve Bevirimat resistance prediction in HIV-1. It turned out that combining structural and sequence-based information in classifier ensembles led to accurate and reliable predictions. Moreover, we were able to identify the most crucial regions for Bevirimat resistance computationally, which are in line with experimental results from other studies.
conclusionsOur analysis demonstrated the use of machine learning techniques to predict HIV-1 resistance against maturation inhibitors such as Bevirimat. New maturation inhibitors are already under development and might enlarge the arsenal of antiretroviral drugs in the future. Thus, accurate prediction tools are very useful to enable a personalized therapy.
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Registered trials
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