Deep Learning data integration for better risk stratification models of bladder cancer

We propose an unsupervised multi-omics integration pipeline, using deep-learning autoencoder algorithm, to predict the survival subtypes in bladder cancer (BC). We used TCGA dataset comprising mRNA, miRNA and methylation to infer two survival subtypes. We then constructed a supervised classification...

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Detalles Bibliográficos
Autores principales: Poirion, Olivier B., Chaudhary, Kumardeep, Garmire, Lana X.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Informatics Association 2018
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5961799/
https://www.ncbi.nlm.nih.gov/pubmed/29888072

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5961799/
https://www.ncbi.nlm.nih.gov/pubmed/29888072

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