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elastic-net-feature-selection

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Use when selecting predictive genes or other molecular features from bulk expression matrices for binary case-vs-control classification with elastic net logistic regression, including coefficient path and cross-validation plots. Trigger keywords: elastic net, glmnet, feature selection, binary classification, lambda.min, lambda.1se. NOT for: survival/Cox modeling, multiclass outcomes, single-cell data, or non-expression tables.

When to use

• Use this skill for binary case-vs-control classification on bulk expression matrices.
• Use it when you need elastic net logistic regression feature selection, coefficient paths, and `cv.glmnet`-based lambda selection.
• Use custom labels such as `Tumor` and `Normal` only when the group file still contains exactly two outcome levels.

When not to use

• Survival or Cox modeling
• Multiclass outcomes
• Single-cell data
• Non-expression tables
• If the group file contains any label outside the requested `case_group` and `control_group`, the command stops with `SKILL_INVALID_DATA` instead of silently dropping samples.
• If either requested class is missing after validation, the command stops with `SKILL_INVALID_DATA`.

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