HIV Resistance Prediction to Reverse Transcriptase Inhibitors: Focus on Open Data
Abstract
:1. Introduction
2. Results and Discussion
2.1. Experimental Methods of HIV-1 Resistance Detection and Concordance between Their Results
2.2. HIV Sequences Repositories
2.2.1. NCBI Nucleotide (GenBank) and NCBI Protein
2.2.2. Los Alamos HIV Sequence Database
2.2.3. The HIV Oligonucleotide Database (HIVoligoDB)
2.2.4. Stanford University HIV Drug Resistance Database
2.3. Computational Methods of HIV-1 RT Associated Resistance and the Level of Concordance between Them
2.4. Perspectives of the HIV Variants’ Open Data Use for HIV Resistance Prediction in Clinical Practice
2.5. Perspectives of the HIV Variants’ Open Data Use for New Drug Development
3. Conclusions
Supplementary Materials
Acknowledgments
Conflicts of Interest
References
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Name of System/Publication | Data Source * | Algorithm | No of HIV Susceptibility Levels/Another Output | Ref |
---|---|---|---|---|
Rega | Proprietary | Rule-based using Boolean expression | 3 levels | [54] |
HIV Grade | Proprietary | Rule-based | 4 levels | [66] |
Geno2Pheno | Proprietary | Decision trees; Support vector machines | Quantitative (Prediction of the FR values) | [55,56] |
Retrogram | Proprietary | Rule-based | 4 levels | [67] |
Antiretroscan | Proprietary | Rule-based | 5 levels | [68] |
HIVTrePS | Proprietary | Random Forests | Estimated probability of the treatment success | [57] |
EuResist | Proprietary | Combined (Bayes network Support Vector Machines, Fuzzy Logic, Case-Based Reasoning and Random Forests) | Estimated probability of the treatment success | [58] |
The application of artificial neural networks for phenotypic drug resistance prediction: evaluation and comparison with other interpretation systems | Freely available (Stanford HIV resistance database) | Artificial neural networks | 2 levels | [69] |
Genotypic predictors of human immunodeficiency virus type 1 drug resistance | Freely available (Stanford HIV resistance database) | Decision trees, neural networks, least-squares regression (LSR), SVR, least angle regression (LARS) | 3 levels | [60] |
Significantly improved HIV inhibitor efficacy prediction employing proteochemometric models generated from Antivirogram data | Proprietary (training), Free available (validation) | Support vector machines | 2 levels of resistance and quantitative prediction of FR value | [61] |
PASS-based approach to predict HIV-1 reverse transcriptase resistance Computational prediction of human immunodeficiency resistance to reverse transcriptase inhibitors. | Freely available (Stanford HIV resistance database) | PASS-based (modified Bayes) approach/ Set of machine learning methods | Estimated probability of the resistance occurrence/ belonging to a class of resistant variants | [62,63] |
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Tarasova, O.; Poroikov, V. HIV Resistance Prediction to Reverse Transcriptase Inhibitors: Focus on Open Data. Molecules 2018, 23, 956. https://doi.org/10.3390/molecules23040956
Tarasova O, Poroikov V. HIV Resistance Prediction to Reverse Transcriptase Inhibitors: Focus on Open Data. Molecules. 2018; 23(4):956. https://doi.org/10.3390/molecules23040956
Chicago/Turabian StyleTarasova, Olga, and Vladimir Poroikov. 2018. "HIV Resistance Prediction to Reverse Transcriptase Inhibitors: Focus on Open Data" Molecules 23, no. 4: 956. https://doi.org/10.3390/molecules23040956