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Fig. 3 | BMC Bioinformatics

Fig. 3

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

Fig. 3

Comparative predictive performance of latent representations and gene-level representation. A The Matthews correlation coefficient (MCC) for the most effective regularization technique across all 30 datasets is presented. The top row shows cross-validation performance with 90% of the data used for training, while the bottom row shows performance with only 20% used for training. The mean MCC difference (Δ) between gene-level and latent representations is indicated, with a negative Δ value signifying better performance of the gene-level representation. B The predictive performance of models with regularization trained on latent representations is displayed across five pairs of independent datasets. Within each pair, one dataset was used for training predictive models, and the paired dataset served as the test set for performance assessment

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BMC Bioinformatics

ISSN: 1471-2105

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