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Fig. 2 | BMC Bioinformatics

Fig. 2

From: Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic data

Fig. 2

Comparative analysis of predictive model performance with and without regularization. The performance of predictive models applied to gene-level data and their latent representations across 30 datasets is shown. The performance is displayed as the test data Matthew correlation coefficient (MCC) obtained through the CV-permutation test for both the predictive model with the best regularization technique and the model without regularization. The CV was executed with two distinct settings: A using 90% of the data samples for training and B utilizing 20% for training

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BMC Bioinformatics

ISSN: 1471-2105

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