sifrimlab/MIMA
Multimodal Integration with Modality-agnostic Autoencoders - Developed by LMIB @ KU Leuven
This project helps biological researchers integrate different types of molecular data, like gene expression and epigenetics, from the same cell samples. It takes in various molecular datasets (in AnnData or MuData format) and produces a unified view of the cell's state, highlighting shared biological signals while correcting for technical variations between experiments. Researchers in genomics, proteomics, or single-cell biology can use this to get a more complete picture of cellular processes.
Use this if you need to combine multiple molecular datasets from the same biological samples to understand complex cellular states and disentangle true biological signals from experimental noise.
Not ideal if your datasets are not paired (i.e., you don't have corresponding observations across all modalities for each sample) or if you are not working with biological molecular data.
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Last pushed
Oct 29, 2025
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