Article

E2EGraph: An End-to-end Graph Learning Model for Interpretable Prediction of Pathlogical Stages in Prostate Cancer

2023-03-12

Abstract excerpt

Prostate cancer is one of the deadliest cancers worldwide. An accurate prediction of pathological stages using the expressions and interactions of genes is effective for clinical assessment and treatment. However, identification of interactions using biological procedure is time consuming and prohibitively expensive. A graph is a powerful representation for the complex interactome of genes, their transcripts, and...

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Literature Corpus work: bfb1d343-991f-54de-b286-cbe95c91447b
DOI: 10.1101/2023.03.09.531924

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