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Fig. 1 | BMC Bioinformatics

Fig. 1

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

Fig. 1

Study overview. Preprocessed gene expression datasets served as input for two supervised dimensionality reduction methods: linear optimal low-rank projection (LOL) and low-rank canonical correlation analysis (CCA), alongside two unsupervised methods: principal component analysis (PCA) and consensus independent component analysis (c-ICA). Furthermore, latent representations were generated using a transfer learning approach with an autoencoder (AE), adversarial variational autoencoder (AVAE), and c-ICA, all trained on the GPL570 dataset. These gene-level and latent representations were then individually utilized in the predictive modeling pipeline, employing a cross-validation strategy with and without three different regularization techniques to evaluate predictive performance. The statistical significance of model performance was determined using a permutation test

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BMC Bioinformatics

ISSN: 1471-2105

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