Multi-omics integration and batch correction using a modality-agnostic deep learning framework
Jose Ignacio Alvira Larizgoitia, Gabriele Partel, Lorenzo Venturelli, Wanqiu Zhang, Xander Spotbeen, Sebastiaan Vanuytven, Sam Kint, Katy Vandereyken, David Wouters, Anis Ismail, Regis Scarceriaux, Jakub Idkowiak, Tassiani Sarretto, Shane R. Ellis, Massimo F. Loda, , Alejandro Sifrim
This paper introduces MIMA, a deep learning framework for multi-omics integration and batch correction, which effectively removes batch effects and preserves biological information across modalities.
bioRxiv · Oct 21, 2025 · PreprintBioinformatics