📚 References & Scientific Sources

Core scientific literature, datasets, and computational frameworks used in the development of the PhenoGenX HIV resistance analytics platform.

🧬 HIV Drug Resistance Databases & Tools
Stanford HIV Drug Resistance Database (HIVDB)

Comprehensive database of HIV drug resistance mutations and interpretation algorithms.

Reference: Shafer, R.W., et al. (2008). HIVDB: An HIV-1 drug resistance database.

Geno2Pheno

Web service for estimating phenotypic drug resistance from HIV-1 genotypes.

Reference: Beerenwinkel, N., et al. (2003). Geno2pheno: Estimating phenotypic drug resistance.

ANRS HIV Drug Resistance Database

French National Agency for AIDS Research resistance interpretation system.

Reference: Masquelier, B., et al. (2005). ANRS HIV-1 resistance interpretation algorithm.

EU Resist Network

European network for surveillance of HIV drug resistance.

Reference: Zazzi, M., et al. (2010). EU Resist standardized genotypic drug resistance interpretation.

Los Alamos HIV Sequence Database

HIV-1 subtype consensus sequences and reference alignments.

Reference: Kuiken, C., et al. (2003). HIV sequence database.

WHO HIV Drug Resistance Reports

Technical guidance and surveillance reports on HIV drug resistance.

Reference: WHO (2021). HIV Drug Resistance Report 2021.

📖 Scientific Literature
  1. Wensing, A. M., et al. (2025)
    2025 Update of the Drug Resistance Mutations in HIV-1.
    Special Contribution, International Antiviral Society–USA. Published ahead of issue March 4, 2025. https://www.iasusa.org/
  2. Beerenwinkel, N., et al. (2003)
    Geno2pheno: Estimating phenotypic drug resistance from HIV-1 genotypes.
    Nucleic Acids Research, 31(13), 3850–3855. doi:10.1093/nar/gkg575
  3. Chu, C., et al. (2022)
    Genotypic Resistance Testing of HIV-1 DNA in Peripheral Blood Mononuclear Cells.
    Clinical Microbiology Reviews, 35(4). doi:10.1128/cmr.00052-22
  4. Cozzi-Lepri, A., et al. (2015)
    Low-frequency drug-resistant HIV-1 and risk of virological failure to first-line NNRTI-based ART.
    Journal of Antimicrobial Chemotherapy, 70(3), 930–940. doi:10.1093/jac/dku426
  5. Goel, R. (2022)
    Artificial intelligence in medicine: its working, potentials and challenges.
    International Journal of Advances in Medicine, 10(1), 108. doi:10.18203/2349-3933.ijam20223412
  6. Günthard, H. F., & Scherrer, A. U. (2016)
    HIV-1 Subtype C, Tenofovir, and the Relationship with Treatment Failure and Drug Resistance.
    Journal of Infectious Diseases, 214(9), 1289–1291. doi:10.1093/infdis/jiw214
  7. Hamers, R. L., et al. (2012)
    Effect of pretreatment HIV-1 drug resistance on immunological, virological, and drug-resistance outcomes.
    The Lancet Infectious Diseases, 12(4), 307–317. doi:10.1016/S1473-3099(11)70255-9
  8. Heneine, W. (2010)
    When do minority drug-resistant HIV-1 variants have a major clinical impact?
    Journal of Infectious Diseases, 201(5), 647–649. doi:10.1086/650545
  9. Joshi, S., et al. (2025)
    AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation.
    Journal of the American Medical Informatics Association, 32(3), 589–594. doi:10.1093/jamia/ocae301
  10. Kantor, R., et al. (2015)
    Pretreatment HIV Drug Resistance and HIV-1 Subtype C Are Independently Associated with Virologic Failure.
    Clinical Infectious Diseases, 60(10), 1541–1549. doi:10.1093/cid/civ102
  11. Kouamou, V., & McGregor, A. M. (2021)
    High Levels of Pre-Treatment HIV Drug Resistance in Zimbabwe: Is this a Threat to HIV/AIDS Control?
    Journal of AIDS and HIV Treatment, 3(3), 42–45.
  12. Larder, B., et al. (2007)
    The development of artificial neural networks to predict virological response to combination HIV therapy.
    Antiviral Therapy, 12(1), 15–24. doi:10.1177/135965350701200112
  13. Noguera-Julian, M., et al. (2016)
    Contribution of APOBEC3G/F activity to the development of low-abundance drug-resistant HIV-1 variants.
    Clinical Microbiology and Infection, 22(2), 191–200. doi:10.1016/j.cmi.2015.10.004
  14. Paredes, R., et al. (2010)
    Pre-existing minority drug-resistant HIV-1 variants, adherence, and risk of antiretroviral treatment failure.
    Journal of Infectious Diseases, 201(5), 662–671. doi:10.1086/650543
  15. Poojitha, D., et al. (2025)
    Enhancing HIV Drug Resistance Prediction Using Bidirectional LSTM Neural Networks.
    Procedia Computer Science, 258, 2888–2898. doi:10.1016/j.procs.2025.04.549
  16. Pou-Prom, C., et al. (2022)
    From compute to care: Lessons learned from deploying an early warning system into clinical practice.
    Frontiers in Digital Health, 4, 1–11. doi:10.3389/fdgth.2022.932123
  17. Revell, A. D., et al. (2010)
    Modelling response to HIV therapy without a genotype: An argument for viral load monitoring in resource-limited settings.
    Journal of Antimicrobial Chemotherapy, 65(4), 605–607. doi:10.1093/jac/dkq032
  18. Rhee, S. Y., et al. (2005)
    HIV-1 protease and reverse-transcriptase mutations: Correlations with antiretroviral therapy in subtype B isolates.
    Journal of Infectious Diseases, 192(3), 456–465. doi:10.1086/431601
  19. Rhee, S., et al. (2019)
    A systematic review of the genetic mechanisms of dolutegravir resistance.
    Journal of Antimicrobial Chemotherapy, 74, 3135–3149. doi:10.1093/jac/dkz256
  20. Rong, G., et al. (2020)
    Artificial Intelligence in Healthcare: Review and Prediction Case Studies.
    Engineering, 6(3), 291–301. doi:10.1016/j.eng.2019.08.015
  21. Rudin, C. (2019)
    Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.
    Nature Machine Intelligence, 1(5), 206–215. doi:10.1038/s42256-019-0048-x
  22. Steiner, M. C., et al. (2020)
    Drug resistance prediction using deep learning techniques on HIV-1 sequence data.
    Viruses, 12(5), 1–24. doi:10.3390/v12050560
  23. Tarasova, O., et al. (2018)
    A computational approach for the prediction of HIV resistance based on amino acid and nucleotide descriptors.
    Molecules, 23(11). doi:10.3390/molecules23112751
  24. Tebit, D. M., & Arts, E. J. (2011)
    Tracking a century of global expansion and evolution of HIV to drive understanding and to combat disease.
    The Lancet Infectious Diseases, 11(1), 45–56. doi:10.1016/S1473-3099(10)70186-9
  25. Wagner, S., et al. (2015)
    Algorithm evolution for drug resistance prediction: Comparison of systems for HIV-1 genotyping.
    Antiviral Therapy, 20(6), 661–665. doi:10.3851/IMP2947
  26. Winand, R., et al. (2015)
    Assessing transmissibility of HIV-1 drug resistance mutations from treated and from drug-naive individuals.
    AIDS, 29(15), 2045–2052. doi:10.1097/QAD.0000000000000811
  27. World Health Organization (2021)
    HIV Drug Resistance Report 2021.
    Technical Report. WHO Report Link
⚙️ Technical Tools & Computational Frameworks
MAFFT

Multiple sequence alignment program for amino acid or nucleotide sequences.

Reference: Katoh & Standley (2013). MAFFT Multiple Sequence Alignment Software.

MUSCLE

Multiple sequence alignment by log-expectation.

Reference: Edgar (2004). MUSCLE: multiple sequence alignment.

Clustal Omega

Multiple sequence alignment program using seeded guide trees and HMM profile-profile techniques.

Reference: Sievers et al. (2011). Clustal Omega.

CRPS Scoring Framework

Continuous Ranked Probability Score for ensemble model optimization and validation.

Reference: Gneiting & Raftery (2007). Strictly Proper Scoring Rules.

Mutation-aware ML Models

Machine learning frameworks incorporating HIV-1 mutation signatures for phenotypic prediction.

Framework: Custom ensemble models (ElasticNet, LightGBM, XGBoost, Random Forest).

📄 Complete Bibliography

A complete DOI-linked bibliography and downloadable BibTeX file will be included in PhenoGenX v1.0.

All references are formatted according to APA 7th edition standards with direct links to original sources where available.