radiology-radiogenomics

radiology-radiogenomics is a Bioinformatics & life sciences skill for AI agents, published by huang-sir1 in the radiology-skills repository on GitHub. Skillspace rates radiology-radiogenomics C (71/100) and it passed the safety scan; the rating is based on static analysis only.

Design, analyse, report, and submit imaging-multi-omics radiogenomics studies that link radiomic/deep imaging phenotypes to genomic, transcriptomic, single-cell, and spatial-omics data. Use when the user mentions radiogenomics, imaging genomics, imaging-transcriptomics, TCIA/TCGA, GEO, dbGaP, EGA, cBioPortal, multi-omics integration, MOFA, iCluster, SNF, DIABLO, scRNA-seq deconvolution, CIBERSORTx, BayesPrism, spatial transcriptomics, Visium, Xenium, imaging habitats, gene expression, mutations, pathways, biological validation, omics QC, sample-to-image mapping, radiogenomics submission packages, or reviewer comments about batch/leakage/spatial mismatch. Covers the full chain from matched-cohort feasibility and protocol/SAP to omics QC, imaging pipeline, integration, validation, reporting, submission, and rebuttal support. Never fabricates associations, cohort counts, accessions, approvals, metrics, or reviewer actions.

Grade

C

Quality

71 / 100

Safety

Passed

GitHub stars

819

Unproven: rated by static analysis only, not yet compared head-to-head.

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