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Table 2 Bootstrapping results for the linear discriminant analysis-derived sample assignments for the six independent datasets. Bootstrapping was performed with 1000 iterations, with average accuracy of correct class assignment used as the evaluation metric. Average accuracy and silhouette values are reported as median and 95% confidence intervals

From: Unsupervised machine learning identifies distinct ALS molecular subtypes in post-mortem motor cortex and blood expression data

Dataset

Number of Genes

Median Accuracy (95% CI)

Median Silhouette

(95% CI)

TargetALS

470

1.000 (1.000–1.000)

0.137 (0.109–0.168)

Zucca

381

1.000 (1.000–1.000)

0.127 (0.0560–0.234)

van Rheenen

535

0.738 (0.693, 0.778)

0.0185 (-0.0145-0.0512)

BrainBank Controls

787

0.661 (0.543–0.780)

0.220 (0.167–0.281)

TargetALS (occipital cortex)

651

1.000 (1.000–1.000)

0.199 (0.132–0.283)

TargetALS (cerebellum)

622

1.000 (1.000–1.000)

0.174 (0.139–0.217)