Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features

Accurate prognosis of patients with cancer is important for the stratification of patients, the optimization of treatment strategies, and the design of clinical trials. Both clinical features and molecular data can be used for this purpose, for instance, to predict the survival of patients censored...

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Veröffentlicht in:	Genes Jg. 10; H. 3; S. 240
Hauptverfasser:	Xie, Gangcai, Dong, Chengliang, Kong, Yinfei, Zhong, Jiang F., Li, Mingyao, Wang, Kai
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 21.03.2019 MDPI
Schlagworte:	Binding sites Cancer Clinical trials Computational Biology - methods Computer applications Copy number Datasets Deep Learning DNA Methylation Gene expression Gene Expression Profiling Genomes Genomics Glioblastoma Glioblastoma multiforme Guanosine diphosphate Humans Machine learning Medical prognosis Mutation Neoplasms - genetics Neoplasms - pathology Normal distribution Patients Prognosis Protein expression Regularization methods Renal cell carcinoma Sample size Simulation Standard scores Survival analysis Urothelial carcinoma deep learning genomics cancer survival analysis
ISSN:	2073-4425, 2073-4425
Online-Zugang:	Volltext
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