Investigating the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model

Background: Survival analysis is an important part of cancer studies. In addition to the existing Cox proportional hazards model, deep learning models have recently been proposed in survival prediction, which directly integrates multi-omics data of a large number of genes using the fully connected d...

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Bibliographic Details
Main Authors:	Feng, J. (Author), Li, F. (Author), Zhang, H. (Author)
Format:	Article
Language:	English
Published:	BioMed Central Ltd 2021
Subjects:	Bioinformatics Cancer development Copy number datum Cox proportional hazards models Deep learning Deep Learning Deep neural networks Diseases Drug combinations Exploratory studies Forecasting Gene expression genetics human Humans Learning systems mortality Multilayer neural networks neoplasm Neoplasms Network layers Neural Networks, Computer proportional hazards model Proportional Hazards Models Signaling Signaling cascades Signaling pathways survival analysis Survival analysis Survival Analysis Survival prediction TCGA
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Investigating the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model

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