Fig. 3: Metrics on the Alzheimer dataset. | Nature Communications

Fig. 3: Metrics on the Alzheimer dataset.

From: BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks

Fig. 3

A Valid and test MCC scores for all methods benchmarked. Higher is better. The conditions compared are Cognitively unimpaired patients and Alzheimer’s Disease with dementia. B Batch mixing metrics: normalized Batch Entropy (nBE), Adjusted Rand Index (ARI), Adjusted Mutual Information (AMI). Smaller is better. C QC metrics: Normalized Median Euclidean distance (nMED) and QC average Pearson Correlation Coefficient (qc_aPCC). Lower nMED and 1-qc_aPCC are better. MCC is compared to D nBE, E ARI, F QC nMED, and G QC aPCC. Error bars represent standard deviations around the means. All error bars are derived from the results of 5-fold cross-validation (n = 5). The BERNN models are underlined. Source data are provided as a Source Data file.

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