Date of Award
attention;Drug side effects;dual-view;Heterogeneous graph neural networks;Link prediction;similarities
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Drug adverse side effects (ASEs) have substantial impacts on public health, healthcare costs, and drug discovery processes. Hospital admissions and emergency department visits are frequently attributed to adverse drug reactions (ADRs), incurring significant expenses. Identification of ASEs during the drug discovery process can slow down and prevent many candidate molecules from being selected as commercial drugs. As medication usage continues to rise, effective management of drug side effects becomes increasingly crucial. Previous works have relied on extracting and utilizing single-perspective drug features such as chemical structure, and topological information, or combining associated information between drugs and other bio-markers using Knowledge Graphs. More recent works learn in series to fuse drug representation from multiple perspectives – (microscopic) drug molecules feature and (macroscopic) over a heterogeneous network (created using a combination of various biological entity associations). In this study, we propose a novel Similarity-based Dual View Heterogeneous Graph Neural Network (SDV-HGNN) that simultaneously learns microscopic/intra-view drug substructures features using its molecular graph representation and macroscopic/inter-view drug and side-effect features over a connectivity-enhanced Drug-Adverse Side Effect Network (DSN). We introduced four additional edges between drugs and three between side effects using multi-context-specific defined similarity metrics. Our approach frames the problem as a binary classification task within the context of link prediction on a graph using a novel SDV-HGNN. We performed 10-fold cross-validation to show the superiority of our model and reported an AUROC of 0.92316±0.0014, AUPR 0.91458 ± 0.0025, and F1 0.85611 ± 0.0020.
Kumar, Mayank, "Similarity-Based Dual View Heterogenous Graph Neural Network Method for Drug Adverse Side Effect Prediction" (2024). Electronic Theses and Dissertations. 9192.
Available for download on Tuesday, February 11, 2025