bio-workflows-biomarker-pipeline

To build a biomarker panel from omics expression data, you can automate the workflow using integrated feature selection like Boruta or LASSO stability. The process trains nested cross-validated classifiers and exports the validated panel with SHAP interpretation results.

The best way to validate a biomarker classifier is using nested cross-validation combined with bootstrap-based confidence estimates. This approach provides robust performance estimates and outputs validation-ready models suitable for testing on external datasets.

Yes, you can use SHAP to interpret biomarker discovery results from transcriptomics data. The workflow applies SHAP-based interpretation to explain classifier decisions, helping identify the contribution of specific features in the final biomarker panel.

Yes, this biomarker pipeline supports multi-omics datasets for feature selection. It applies to expression datasets including transcriptomics and proteomics, using Boruta or LASSO stability selection to identify robust biomarker signatures.

Use LASSO stability selection for biomarker discovery to reduce overfitting and ensure feature selection robustness. It integrates with nested cross-validation to provide reliable biomarker panels and bootstrap-based confidence estimates for performance.

You export a trained biomarker classifier for external validation by completing the nested cross-validated training workflow. The process outputs the biomarker panel and the trained model, making them directly suitable for external validation and prospective studies.