Article

GAE-Δ: A Graph-Learning Framework for Gene Network Rewiring and Clinical Outcome Prediction from Multi-Omics Data

2026-05-26

Abstract excerpt

Cancer progression and outcomes are driven in part by changes to molecular networks that result from genetic and/or environmental perturbations. These network changes manifest across multiple interconnected network layers and include accumulation of somatic mutations, altered proteinprotein interactions and dysregulated gene-expression. Here we describe a graph autoencoder–based framework (Graph Autoencoder-Delta...

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Literature Corpus work: 6181981d-c2e6-5233-8533-c9491b5422da
DOI: 10.64898/2026.05.21.726880

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