Single Cell Analysis
Showing 10 of 10 projects
A curated list of deep learning implementations and resources for biological research, with a focus on genomics.
A Python library for deep probabilistic modeling and analysis of single-cell and spatial omics data.
A transformer-based foundation model pretrained on millions of single-cell profiles for generative AI tasks in single-cell multi-omics.
A collection of transformer-based foundation models for genomics and transcriptomics, enabling tasks like sequence analysis, functional prediction, and conversational DNA exploration.
R toolkit for inference, visualization and analysis of cell-cell communication from single-cell transcriptomics data.
A scalable Python toolkit for analyzing and visualizing spatial molecular data from tissue sections.
A deep learning library for single-cell analysis of biological images, specializing in cell segmentation and tracking.
A deep learning framework for integrating single-cell multi-omics data using graph-linked unified embeddings.
A probabilistic cell segmentation method for spatial transcriptomics data from platforms like Xenium, CosMx, MERSCOPE, and Visium HD.
A curated collection of databases, software, and papers for computational biology research.
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