bio-machine-learning-biomarker-discovery

To identify biomarkers in high-dimensional omics data, this skill applies Boruta, mRMR, and LASSO feature selection methods to process expression matrices and output a ranked list of informative features. It handles large feature spaces with small sample sizes.

Reducing redundancy in biomarker panels is handled by applying mRMR (minimum redundancy maximum relevance) and LASSO regularization, which work together to minimize feature redundancy while maximizing relevance for compact, interpretable biomarker sets.

Stability selection for robust biomarker discovery is performed by running feature selection across bootstrap samples, evaluating univariate pre-filtering alongside Boruta and LASSO to ensure only consistently selected features are ranked in the final output list.

Yes, this workflow is specifically designed for cancer omics datasets with small sample sizes and thousands of features, applying univariate pre-filtering and stability selection to build robust biomarker panels for disease classification.

Yes, the workflow supports Boruta all-relevant feature selection to capture all meaningful biomarkers, complemented by mRMR for compact panels and LASSO for sparse, interpretable feature sets using Python libraries like BorutaPy and scikit-learn.

Omics feature selection and biomarker ranking require Python libraries including BorutaPy for all-relevant selection, mrmr for minimum redundancy, and scikit-learn for LASSO regularization to process matrix data and output annotated feature lists.