Minimal code and result bundle for the C. elegans division-prediction project.
code/geo_matched_gen/scripts/: key scripts for single-gene baselines, multi-gene alignment, classifier training, and GNN gene-feature tests.code/geo_matched_gen/experiment*/: core experiment summaries and the maintrain_exp3.pyimplementation.results/report0317/: current report, plots, manifests, and summary CSVs.results/report0309/: gene-coverage summary tables used for panel selection.
- Raw EPIC CSV data.
- Large generated intermediate datasets.
- Temporary logs / caches / local-only artifacts.
- Single-gene expression is weak; size is a strong baseline.
- Multi-gene alignment adds signal, especially with donor ensembling.
- Classifier performance peaks around a focused 10--11 gene panel.
- In the GNN, gene features help when injected into event heads rather than concatenated into the trunk.