An Unsupervised Deep Learning-Based Model Using Multiomics Data to Predict Prognosis of Patients with Stomach Adenocarcinoma

METHODS: Patients (363 in total) with stomach adenocarcinoma from The Cancer Genome Atlas (TCGA) cohort were included. An autoencoder was constructed to integrate the RNA sequencing, miRNA sequencing, and methylation data. The features of the bottleneck layer were used to perform the k-means cluster...

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Detalles Bibliográficos
Autores principales: Chen, Sizhen, Zang, Yiteng, Xu, Biyun, Lu, Beier, Ma, Rongji, Miao, Pengcheng, Chen, Bingwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9633210/
https://www.ncbi.nlm.nih.gov/pubmed/36339684
http://dx.doi.org/10.1155/2022/5844846

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9633210/
https://www.ncbi.nlm.nih.gov/pubmed/36339684
http://dx.doi.org/10.1155/2022/5844846

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