Prediction of HIV-1 sensitivity to broadly neutralizing antibodies using statistical distribution sampling (SDS)...

Area of Science:

• Virology
• Immunology
• Computational Biology
• Machine Learning

Background:

• Accurate prediction of HIV-1 sensitivity to broadly neutralizing antibodies (bNAbs) is vital for developing effective HIV therapies and prevention strategies.
• Traditional experimental methods for assessing bNAb sensitivity are resource-intensive and lack scalability due to HIV-1's genetic diversity.
• Existing machine learning methods often overlook class imbalance and systematic biases present in multi-laboratory HIV-1 datasets.

Purpose of the Study:

• To develop a novel statistical framework, the Statistical Distribution Sampling (SDS) method, to accurately predict HIV-1 sensitivity to bNAbs.
• To address and overcome limitations of current machine learning approaches, specifically class imbalance and cross-institutional variance in HIV-1 datasets.
• To enhance the robustness and scalability of predicting antibody efficacy against diverse HIV-1 strains.

Main Methods:

• The Statistical Distribution Sampling (SDS) method converts amino acid sequences into numerical vectors using the k-string method.
• Antibody effectiveness is determined by IC50/IC80 thresholds, followed by stratified random sampling to generate a Sampling generator.
• Histogram analysis and kernel density estimation characterize vector dimension distributions, enabling sampling to create the SDS matrix, integrated with Random Forest for predictions.

Main Results:

• The SDS method demonstrated statistically superior Area Under the Curve (AUC) and Accuracy (ACC) performance compared to LBUM and SLAPNAP benchmarks across 10 independent trials.
• The proposed method exhibited significantly lower standard deviation across multiple runs, indicating consistent robustness in predicting antibody efficacy.
• The SDS approach enables comprehensive prediction of all database sequences, achieving strong predictive performance in HIV-1 sensitivity analysis.

Conclusions:

• The Statistical Distribution Sampling (SDS) method offers a robust and accurate framework for predicting HIV-1 sensitivity to bNAbs, outperforming existing benchmarks.
• This novel approach effectively addresses data imbalance and cross-institutional variance, crucial for reliable machine learning models in HIV research.
• The SDS method advances the development of therapeutic and preventive strategies against HIV-1 by providing a scalable and consistent prediction tool.