MCP server for spatial transcriptomics analysis through natural language interfaces.
- bioinformatics
- cell-communication
- deconvolution
- machine-learning
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MCP server for spatial transcriptomics analysis through natural language interfaces.
CancerFoundation: A single-cell RNA sequencing foundation model to decipher drug resistance in cancer
Deciphering cell-cell communication from spatially resolved transcriptomic data at single-cell resolution with subgraph-based attentional graph neural network
A versatile single-cell cross-modality translation method via dual-aligned variational autoencoders
Fast, deterministic cell-type deconvolution for million-bin spatial transcriptomics
PyTorch implementation of "TGSA: Protein-Protein Association-Based Twin Graph Neural Networks for Drug Response Prediction with Similarity Augmentation"
Official implementation of JAMIE, Joint variational Autoencoders for Multimodal Imputation and Embedding
A flexible Julia toolkit for high-dimensional cellular profiles
DetecDiv provides a comprehensive set of tools to analyze time microscopy images using deep learning methods. The software structure is such that data can be processed either at the command line or using a graphical user-interface. Detecdiv classification models include : image classification and regression, semantic segmentation, LSTM networks to analyze data and image timeseries.
XitoSBML - the spatial SBML plugin for ImageJ
Interpretable modeling of cellular responses to drug perturbations using cycle consistence learning
gateway for segmentation data to Rstats & visualization with ggplot2 - in development