Update README.md

README.md

@@ -3,13 +3,6 @@ license: mit
 ---
 Ensemble approach to predicting outcomes in lower grade glioma based on GSL gene expression and metabolic simulation
 
-Models trained on TCGA and CGGA LGG data (Initial models trained on TCGA and validated against CGGA). This repo contains:
-- `models/`:
-  - `SVM.pkl`, `RandomForest.pkl`, `XGBoost.pkl`, `LogisticRegression.pkl` — sklearn pipelines
-  - `ANN_pipeline.pkl` — full sklearn Pipeline with skorch wrapper
-  - `ANN_params.pt` — PyTorch weights for ANN (optional if pipeline already includes)
-- `ensemble/`:
-  - `scaler.pkl` — StandardScaler used for ensemble logits
-  - `beta_vec.npy` — Cox coefficients for ensemble
-  - `ens_threshold.json` — tuned ensemble threshold
-- `thresholds/per_model_thresholds.json`
+Models trained on TCGA and CGGA LGG data (Initial models trained on TCGA and validated against CGGA).
+
+Will update repo contents information.