MetastaSite: Predicting metastasis to different sites using deep learning with gene expression data.

ABSTRACT: Deep learning has massive potential in predicting phenotype from different omics profiles. However, deep neural networks are viewed as black boxes, providing predictions without explanation. Therefore, the requirements for these models to become interpretable are increasing, especially in the medical field. Here we propose a computational framework that takes the gene expression profile of any primary cancer sample and predicts whether patients' samples are primary (localized) or metastasized to the brain, bone, lung, or liver based on deep learning architecture. Specifically, we first constructed an AutoEncoder framework to learn the non-linear relationship between genes, and then DeepLIFT was applied to calculate genes' importance scores. Next, to mine the top essential genes that can di

SUBMITTER: Albaradei S 

PROVIDER: S-EPMC9353773 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature