adamhoffman2155-hue/project-6-gea-survival-pipeline

This is a 6-day-old student/personal project with zero adoption signals (0 stars, 0 forks, no activity). It assembles well-known components (Snakemake + DuckDB + scikit-survival + Streamlit) into a standard bioinfomatics pipeline workflow. The architecture is conventional: data ingestion → feature engineering → model training → interactive interface. No novel methodological contribution is evident from the description. The survival analysis approach (Cox PH) is 50+ years old and standard in clinical genomics. TCGA-STAD is public data, not proprietary. While the specific end-to-end integration may be useful locally, it lacks domain depth (no custom feature selection, no domain-specific survival models, no validation on holdout clinical cohorts) and has negligible community traction. Frontier labs (Google Health, Anthropic, OpenAI) have already deployed clinical ML systems and could trivially add or replicate this pipeline as a component. The project does not establish switching costs, network effects, or defensible IP. It is a reference implementation of commodity tooling applied to a public dataset.