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This project is a comparative study of Autoencoder (AE) and Principal Component Analysis (PCA) for dimensionality reduction in gene expression data. It aims to understand the unique capabilities and applications of both methods in handling high-dimensional biological data.

License: GNU General Public License v3.0

R 4.04% Jupyter Notebook 89.19% Python 1.42% Nextflow 5.36%
ae autoencoders bioinformatics bioinformatics-tool dimensionality-reduction geneexpression machine-learning pca autoencodervspca

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