GAE-Δ: A Graph-Learning Framework for Gene Network Rewiring and Clinical Outcome Prediction from Multi-Omics Data (opens in new tab)

Cancer progression and outcomes are driven in part by changes to molecular networks thatresult from genetic and/or environmental perturbations. These network changes manifestacross multiple interconnected network layers and include accumulation of somatic mutations, altered protein-protein interactions and dysregulated gene-expression. Here wedescribe a graph autoencoder based framework (Graph Autoencoder-Delta (GAE-{Delta})), for characterizing phenotype-specific gene role shifts across mult...