zamushwani/biomedical-ai-skills
Command Claude Code
Differential expression from a count matrix with DESeq2. Use when the user has bulk RNA-seq counts and wants DEGs, a volcano plot, or asks which genes differ between conditions.
zamushwani/biomedical-ai-skills
Command Claude Code
Spatial transcriptomics analysis: loading, spatial QC, spatially variable genes, deconvolution, domains. Use for Visium, Xenium, MERSCOPE, or CosMx data.
zamushwani/biomedical-ai-skills
Command Claude Code
Annotate and clinically interpret variants from a VCF. Use when the user has variant calls and wants functional annotation, ACMG classification, oncogenicity, or clinical tiers.
zamushwani/biomedical-ai-skills
Command Claude Code
Estimate immune cell composition from bulk RNA-seq. Use when the user asks about tumour microenvironment, immune infiltration, CIBERSORT/quanTIseq/xCell, or immune cell fractions.
zamushwani/biomedical-ai-skills
Command Claude Code
Fit dose-response curves and compute IC50/AUC. Use when the user has viability data across drug concentrations, or asks about IC50, EC50, or drug sensitivity.
zamushwani/biomedical-ai-skills
Command Claude Code
Kaplan-Meier curves and Cox models from clinical data. Use when the user asks about survival, prognosis, hazard ratios, or wants a KM plot.
zamushwani/biomedical-ai-skills
Command Claude Code
Quality control and filtering for single-cell RNA-seq. Use when the user has an h5ad/h5/10x matrix and wants QC, cell filtering, doublet detection, or asks why their cell count dropped.
zamushwani/biomedical-ai-skills
Command Claude Code
Query TCGA/GDC for projects, mutations, or clinical data. Use when the user asks for TCGA cohorts, mutation counts, or clinical variables from the Genomic Data Commons.
zamushwani/biomedical-ai-skills
Command Claude Code
Pathway enrichment on a ranked gene list (GSEA or ORA). Use when the user asks which pathways are enriched, wants GSEA/fgsea, or has DE results to interpret biologically.
zamushwani/biomedical-ai-skills
Command Claude Code
Tile a whole-slide image with tissue detection for downstream modelling. Use for .svs/.ndpi/.mrxs slides, OpenSlide, or when the user asks about patches, tiles, or WSI preprocessing.
zamushwani/biomedical-ai-skills
Cursor rule Cursor
Biomedical AI Skills — protocol files for cancer bioinformatics, and the rules for changing them.
zamushwani/biomedical-ai-skills
Instructions file CodexOpenCode
AGENTS.md instructions for zamushwani/biomedical-ai-skills, covering agents.md, using a skill, working in this repository, commands and layout.
zamushwani/biomedical-ai-skills
Instructions file
Claude Code instructions for zamushwani/biomedical-ai-skills, covering biomedical ai skills, layout, the five non-negotiables, verification gates and gate 1 — before writing about any package.
zamushwani/biomedical-ai-skills
Instructions file Gemini CLI
Gemini CLI instructions for zamushwani/biomedical-ai-skills, covering gemini.md, short version and commands.
zamushwani/biomedical-ai-skills
Skill Claude CodeCodex
Building Model Context Protocol servers that give AI agents structured, tested access to biomedical databases. Covers MCP server design, the GDC REST API behind TCGA, tool design for search and retrieval, pagination and caching, and the data-shape traps that make a naive wrapper wrong. Part 1 covers TCGA/GDC, Part 2…
zamushwani/biomedical-ai-skills
Skill Claude CodeCodex
Integrated analysis of expression, mutation, copy number, and methylation data from TCGA and GEO for solid tumor characterization.
zamushwani/biomedical-ai-skills
Skill Claude CodeCodex
Predictive biomarkers for immune checkpoint blockade: what PD-L1 IHC scores are and why expression data cannot produce them, tumour mutational burden, microsatellite instability, and the expression signatures (IFN-gamma, TIS, TIDE) that are computable from RNA. Written to keep the assay-derived biomarkers and the…
zamushwani/biomedical-ai-skills
Skill Claude CodeCodex
Information extraction from clinical free text. Covers note parsing and sectioning, biomedical named entity recognition, concept normalization to UMLS and ICD-10, assertion and negation detection, temporal relation extraction, adverse event identification, and de-identification. Written for public and credentialed…
zamushwani/biomedical-ai-skills
Skill Claude CodeCodex
Whole-slide image processing and slide-level modelling for cancer histopathology. Covers reading vendor formats with OpenSlide, the coordinate and resolution semantics that cause most WSI bugs, tissue detection, tile extraction, stain normalization, H&E colour deconvolution, pathology foundation models as tile…
zamushwani/biomedical-ai-skills
Skill Claude CodeCodex
Dose-response modeling and drug sensitivity prediction in cancer cell lines. Covers IC50 and AUC estimation from viability curves, retrieval and harmonization of GDSC, CTRP, and PRISM data through PharmacoGx, DepMap dependency and drug data, sensitivity prediction with regularized regression, and pharmacogenomic…
zamushwani/biomedical-ai-skills
Skill Claude CodeCodex
ATAC-seq and ChIP-seq analysis for chromatin accessibility and transcription factor binding. Covers the filtering that precedes peak calling, ATAC-specific peak calling, differential binding and the DiffBind 3.x changes that silently alter results, motif enrichment, TF activity from accessibility, and assigning peaks…
zamushwani/biomedical-ai-skills
Skill Claude CodeCodex
When to use scGPT, Geneformer, UCE, and the perturbation models, and when a linear baseline beats them. Covers zero-shot embedding extraction, fine-tuning for annotation, in-silico perturbation, and the benchmark evidence behind each recommendation.
zamushwani/biomedical-ai-skills
Skill Claude CodeCodex
Estimate immune and stromal cell composition from bulk RNA-seq using multiple algorithms. Wraps CIBERSORT, quanTIseq, EPIC, xCell, MCP-counter, TIMER, and ESTIMATE through the immunedeconv unified interface.
zamushwani/biomedical-ai-skills
Skill Claude CodeCodex
Systematic review and meta-analysis for clinical and preclinical evidence. Covers protocol registration, search strategy, screening, PRISMA 2020 flow diagrams, risk of bias, pooling with fixed and random effects models, small-study effects, sensitivity diagnostics, network meta-analysis, and GRADE/CINeMA certainty…