ArticleiScience2026
The MOGA multi-modal framework based on graph augmentation networks for drug response prediction.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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Abstract
Personalized cancer treatment faces challenges due to tumor heterogeneity and limitations in existing computational methods regarding multi-omics integration. To address these limitations, this study presents MOGA, an advanced framework for drug response prediction. MOGA constructs a heterogeneous graph integrating cell line multi-omics, drug chemical structures, and response data, utilizing a relational graph convolutional network (RGCN) to model complex interactions. Crucially, a type-specific graph augmentation strategy is proposed, which categorizes neighbor influence to preserve critical sensitive signals while reducing noise from non-sensitive nodes. Experimental results demonstrate that MOGA significantly outperforms competitive baselines in both AUROC and AUPR. Ablation studies and case analysis confirm that the integration of multi-omics data and the targeted augmentation mechanism are central to these performance gains, validating MOGA's potential for precision oncology.
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