AI agent skills for cancer biology and multi-omics research
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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…
zamushwani/biomedical-ai-skills
Skill Claude CodeCodex
Joint analysis of two or more molecular layers on the same samples. Covers method selection, the preprocessing that decides whether integration works at all, MOFA+ factor analysis, similarity network fusion, joint clustering for cancer subtyping, supervised integration, and survival models built on integrated features.
zamushwani/biomedical-ai-skills
Skill Claude CodeCodex
Genomic predictors of radiation response: DNA damage repair pathway profiling, the Radiosensitivity Index and the Genomic-Adjusted Radiation Dose built on it, post-irradiation immune activation signatures, and an honest account of what the abscopal effect can and cannot be predicted from.
zamushwani/biomedical-ai-skills
Skill Claude CodeCodex
Full single-cell RNA-seq pipeline from raw counts to biological interpretation. Covers QC, normalization, batch integration, clustering, annotation, pseudobulk DE, trajectory inference, cell-cell communication, and TF activity. Dual-language: Seurat v5 (R) and scanpy (Python).
zamushwani/biomedical-ai-skills
Skill Claude CodeCodex
Processing and analysis of spatially resolved transcriptomics across sequencing-based (Visium, Visium HD, Slide-seq, Stereo-seq) and imaging-based (Xenium, MERSCOPE, CosMx) platforms. Covers loading, QC, normalization, and spatially variable gene detection. Dual-language: Python (squidpy/SpatialData) and R…
zamushwani/biomedical-ai-skills
Skill Claude CodeCodex
Time-to-event analysis for cancer clinical data. Covers Kaplan-Meier, Cox proportional hazards, competing risks, restricted mean survival time, and optimal cutpoint selection using the survival, ggsurvfit, tidycmprsk, and survRM2 packages.
zamushwani/biomedical-ai-skills
Skill Claude CodeCodex
Annotation and clinical interpretation of DNA variants in cancer. Covers VCF normalization and filtering, functional annotation with VEP, germline classification under ACMG/AMP, somatic classification under the AMP/ASCO/CAP tiers and the ClinGen/CGC/VICC oncogenicity standard, tumor mutational burden, microsatellite…
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