Machine learning for Analysis of Proteomics in Spatial biology - Nature Communications
- cell-annotation
- multiplexed-imaging
- mpif
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Machine learning for Analysis of Proteomics in Spatial biology - Nature Communications
Run scripts, explore your genome, and get personalized explanations — all in natural dialogue.
High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations
MCP server for spatial transcriptomics analysis through natural language interfaces.
The d-SEAMS C++ core engine
Predicting Cell Health with Morphological Profiles
A foundation model-driven whole slide image-scale cell phenotyping method with QuPath integration Resources
Partial Commutative Monoids
3D shape analysis using deep learning
Fast, deterministic cell-type deconvolution for million-bin spatial transcriptomics
Accurate segmentation of bacterial microscope images using deep learning synthetically generated image data.
Data logistics system enabling real-time pathogen surveillance. Built for the Seattle Flu Study.
Ruby client for interacting with the NASA (POWER) Agroclimatology Web Resource
A flexible Julia toolkit for high-dimensional cellular profiles
scPair: Boosting single cell multimodal analysis by leveraging single cell atlases and implicit feature selection
Winner of the Golem Gitcoin Hackathon 2020 (https://blog.golemproject.net/meet-the-winners-golem-gitcoin-hackathon-2020/). This app lets people design, simulate, and analyse antenna arrays without the need to utilize commercial and expensive applications.
Simple webapp for generating small ligand conformers
XitoSBML - the spatial SBML plugin for ImageJ
Histology Topography Cytometry Analysis Toolbox