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Fig. 2 | BMC Genomics

Fig. 2

From: Architectures and accuracy of artificial neural network for disease classification from omics data

Fig. 2

Performance of six architectures of MLP/CNN in classifying 37 datasets. Values in each column were scaled. Architectures were ordered by the mean rank of performance across all 37 datasets (“Aggregate” bar). TCGA transcriptome data were employed for both stage classification (12 cases) and cancer/normal classification (*, 14 cases). Five original NSCLC datasets were organized into nine datasets for stage classification (5 datasets) and histology classification (4 datasets), separately. Two metabolome datasets for chronic kidney disease were adopted to perform classification among 6 classes

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