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Fig. 2 | BMC Medical Informatics and Decision Making

Fig. 2

From: Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis

Fig. 2

Overall pipeline for survival analysis. We obtain multi-omics data (i.e., gene expression, DNA methylation, miRNA expression, and copy number variation) for breast cancer patients from the TCGA-BRCA database. The multi-omics data are preprocessed and normalized to a range of 0 to 1. We then apply four-fold cross-validation and split the data into a training set (60%), validation set (15%), and testing set (25%) in each fold. We train the feature selection or dimension reduction step and the survival networks using the training set and apply them to the validation set for parameter selection and the testing set for performance reporting

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