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Fig. 4 | BMC Bioinformatics

Fig. 4

From: DeepMF: deciphering the latent patterns in omics profiles with a deep learning method

Fig. 4

DeepMF’s imputation and factorization effect on cancer data sets with 70% random dropout. a-d The heatmap presentation and Silhouette width of four cancer data sets with 70% random dropout. The gray tiles in heatmap indicate missing entries. From left to right: matrix with 70% random dropout, after mean impute, after SVDImpute, after DeepMF. a Medulloblastoma data set b Leukemia data set c TCGA BRCA data set d SRBCT data set e Clustering accuracy of cancer subtyping on sample latent matrices generated by two imputations and five matrix factorization tools on different cancer data sets with 70% random dropout

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