Biomedical Informatics at ETH Zurich
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- aestetik Public
AESTETIK: Convolutional autoencoder for learning spot representations from spatial transcriptomics and morphology data
ratschlab/aestetik’s past year of commit activity - DeepSpot Public
DeepSpot: Deep learning model for predicting spatial transcriptomics from H&E histopathology images. Supports spot-level (Visium) and single-cell (Xenium) resolution.
ratschlab/DeepSpot’s past year of commit activity - DeepSpot2Cell Public
DeepSpot2Cell: Predicting virtual single-cell spatial transcriptomics from H&E images using spot-level supervision
ratschlab/DeepSpot2Cell’s past year of commit activity - sigmoni Public Forked from vikshiv/sigmoni
Nanopore signal-based classification using compressed indexing and matching statistics
ratschlab/sigmoni’s past year of commit activity - DeepSpotM Public
Multimodal foundation model predicting transcriptome-wide virtual spatial transcriptomics from histology.
ratschlab/DeepSpotM’s past year of commit activity - ReadBouncer Public Forked from JensUweUlrich/ReadBouncer
Fast and scalable nanopore adaptive sampling
ratschlab/ReadBouncer’s past year of commit activity - RawHash Public Forked from CMU-SAFARI/RawHash
RawHash can accurately and efficiently map raw nanopore signals to reference genomes of varying sizes (e.g., from viral to a human genomes) in real-time without basecalling. Described by Firtina et al. (published at https://academic.oup.com/bioinformatics/article/39/Supplement_1/i297/7210440).
ratschlab/RawHash’s past year of commit activity - MinknoApiSimulator Public
ratschlab/MinknoApiSimulator’s past year of commit activity
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