ArticleBMC bioinformatics2016
SHIVA - a web application for drug resistance and tropism testing in HIV.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- 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
- NeuralBeds: Neural embeddings for efficient DNA data compression and optimized similarity search.Computational and structural biotechnology journal · 2024Article
- Article
- Incorporating temporal dynamics of mutations to enhance the prediction capability of antiretroviral therapy's outcome for HIV-1.Bioinformatics (Oxford, England) · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Web Service for HIV Drug Resistance Prediction Based on Analysis of Amino Acid Substitutions in Main Drug Targets.Viruses · 2023Article
- Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification.Bioinformatics (Oxford, England) · 2023Article
- AI-based multi-PRS models outperform classical single-PRS models.Frontiers in genetics · 2023Article
- Guideline for software life cycle in health informatics.iScience · 2022Review
- Assessment of a Computational Approach to Predict Drug Resistance Mutations for HIV, HBV and SARS-CoV-2.Molecules (Basel, Switzerland) · 2022Article
- Prediction and molecular field view of drug resistance in HIV-1 protease mutants.Scientific reports · 2022Article
- Predicting HIV drug resistance using weighted machine learning method at target protein sequence-level.Molecular diversity · 2021Article
- Fostering reproducibility, reusability, and technology transfer in health informatics.iScience · 2021Review
- HIV primary drug resistance and associated HIV risk factors among HIV positive blood donors in Brazil from 2007 to 2017.Transfusion medicine (Oxford, England) · 2021Article
- Article
- An Innovative Sequence-to-Structure-Based Approach to Drug Resistance Interpretation and Prediction: The Use of Molecular Interaction Fields to Detect HIV-1 Protease Binding-Site Dissimilarities.Frontiers in chemistry · 2020Article
- HIV drug resistance prediction with weighted categorical kernel functions.BMC bioinformatics · 2019Article
- geno2pheno[ngs-freq]: a genotypic interpretation system for identifying viral drug resistance using next-generation sequencing data.Nucleic acids research · 2018Article
- Global HIV Antiretroviral Drug Resistance: A Perspective and Report of a National Institute of Allergy and Infectious Diseases Consultation.The Journal of infectious diseases · 2017Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDrug resistance testing is mandatory in antiretroviral therapy in human immunodeficiency virus (HIV) infected patients for successful treatment. The emergence of resistances against antiretroviral agents remains the major obstacle in inhibition of viral replication and thus to control infection. Due to the high mutation rate the virus is able to adapt rapidly under drug pressure leading to the evolution of resistant variants and finally to therapy failure.
resultsWe developed a web service for drug resistance prediction of commonly used drugs in antiretroviral therapy, i.e., protease inhibitors (PIs), reverse transcriptase inhibitors (NRTIs and NNRTIs), and integrase inhibitors (INIs), but also for the novel drug class of maturation inhibitors. Furthermore, co-receptor tropism (CCR5 or CXCR4) can be predicted as well, which is essential for treatment with entry inhibitors, such as Maraviroc. Currently, SHIVA provides 24 prediction models for several drug classes. SHIVA can be used with single RNA/DNA or amino acid sequences, but also with large amounts of next-generation sequencing data and allows prediction of a user specified selection of drugs simultaneously. Prediction results are provided as clinical reports which are sent via email to the user.
conclusionsSHIVA represents a novel high performing alternative for hitherto developed drug resistance testing approaches able to process data derived from next-generation sequencing technologies. SHIVA is publicly available via a user-friendly web interface.
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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.