A graph neural network-based model with out-of-distribution robustness for enhancing antiretroviral therapy outcome prediction for HIV-1
Giulia Di Teodoro, Federico Siciliano, Valerio Guarrasi, Anne–Mieke Vandamme, Valeria Ghisetti, Anders Sönnerborg, Maurizio Zazzi, Fabrizio Silvestri, Laura Palagi
Abstract
Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge, especially when the ART includes drugs with limited effectiveness data. This scarcity of data can arise either due to the introduction of a new drug to the market or due to limited use in clinical settings, resulting in clinical dataset with highly unbalanced therapy representation. To tackle this issue, we introduce a novel joint fusion model, which combines features from a Fully Connected (FC) Neural Network and a Graph Neural Network (GNN) in a multi-modality fashion. Our model uses both tabular data about genetic sequences and a knowledge base derived from Stanford drug-resistance mutation tables, which serve as benchmark references for deducing in-vivo treatment efficacy based on the viral genetic sequence. By leveraging this knowledge base structured as a graph, the GNN component enables our model to adapt to imbalanced data distributions and account for Out-of-Distribution (OoD) drugs. We evaluated these models’ robustness against OoD drugs in the test set. Our comprehensive analysis demonstrates that the proposed model consistently outperforms the FC model. These results underscore the advantage of integrating Stanford scores in the model, thereby enhancing its generalizability and robustness, but also extending its utility in contributing in more informed clinical decisions with limited data availability. The source code is available at https://anonymous.4open.science/r/hiv-gnn/ . • Introduce a joint fusion model of an FC and GNN for HIV-1 therapy prediction. • Utilize Stanford scores in GNN to enhance model robustness. • Address the clinical need for predicting therapies with novel drugs with limited or no data. • Show our model consistently outperforms baseline models across diverse drugs. • Demonstrate robustness to out-of-distribution drugs, crucial for real world use.