bio-metabolomics-lipidomics
Specialized lipidomics analysis for lipid identification, quantification, and pathway interpretation. Covers LC-MS lipidomics with LipidSear…
Maintainer FreedomIntelligence · Last updated April 1, 2026
Deep visual proteomics: spatial proteomic analysis from laser-capture microdissection MS data.
Original source
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/deep-visual-proteomics-agent
Skill Snapshot
Source Doc
AI Image Segmentation: Deep learning models segment cells and identify phenotypes from brightfield, H&E, or immunofluorescence images.
Phenotype Classification: CNN/transformer classifiers identify cell types, disease states, and morphological abnormalities.
LMD Coordinate Generation: Automated generation of laser microdissection coordinates for cells of interest.
MS Data Integration: Processes MaxQuant/DIA-NN output to link protein abundances to spatial coordinates.
Spatial Proteome Mapping: Creates spatially-resolved proteome maps linking morphology to molecular profiles.
Biologically-Informed Analysis: Neural networks incorporating pathway knowledge for interpretable biomarker discovery.
User: "Identify tumor vs. stroma cells in this H&E image and generate proteome profiles for each population."
Agent Action:
| Component | Tool/Method | Description |
|---|---|---|
| Segmentation | Cellpose, StarDist | Instance segmentation of cells |
| Classification | Custom CNN/ViT | Phenotype assignment |
| LMD Interface | Leica LMD7, PALM | Coordinate export formats |
| MS Processing | MaxQuant, DIA-NN | Protein quantification |
| Integration | Custom Python | Spatial mapping |
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