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Batch Effect Removal via Batch-Free Encoding
Biological measurements often contain systematic errors, also known as "batch effects", which may invalidate downstream analysis when not handled correctly. The problem of removing batch effects is of major importance in the biological community. Despite recent advances in this direction via deep learning techniques, most current methods may not fully preserve the true biological patterns the data contains. In this work we propose a deep learning approach for batch effect removal. The crux ofdoi:10.1101/380816 fatcat:pzrwqmap5bg6rng5jdedmg3fre