Integrative Prediction Strategy (IPS): Blood-based Prediction of Brain Gene Expression

Integrative Prediction Strategy (IPS) predicts brain gene expression from blood-derived features using pre-trained machine learning models. The workflow integrates multiple feature selection strategies (unsupervised and supervised) and evaluates model performance across cross-validation folds. **Workflow Steps:** 1. Load matched blood and brain gene expression data. 2. Apply feature selection: - Unsupervised feature selection (features selected without using the target brain gene) - Supervised feature selection (features selected based on association with the target brain gene) 3. Load pre-trained models (5-fold cross-validation). 4. Select prediction mode: best, top10, or all models. 5. Generate predicted brain expression values. 6. Evaluate predictions (correlation with observed expression). 7. Save outputs to file. **Inputs:** Blood features and matched brain expression data, pre-trained models, selected feature sets, cross-validation fold assignments. **Outputs:** Predicted brain expression values, correlation metrics, model-specific prediction results. **Resource Identification:** RRID: SCR_027608