GEO DataSets

(Submitter supplied) We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: (a) implication of three different normalization techniques, and (b) implication of differential analysis using the generalized linear model (GLM). more...

Organism:

Homo sapiens

Type:

Methylation profiling by high throughput sequencing

Platform:

GPL16791

77 Samples

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