Individual skill
elastic-net-feature-selection
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.
awesome-med-research-skills/Data Analysis/elastic-net-feature-selection/SKILL.md
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- bioinformatics Repository topic
- computational-biology Repository topic
- data-analysis Repository topic
- ai Repository topic
- biology Repository topic
- cancer-genomics Repository topic
- data-visualization Repository topic
- differential-expression Repository topic
- genomics Repository topic
- medical Repository topic
- engineering Derived category
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SKILL.mdcore · 11,416 bytesSKILL.mdcore · 11,416 bytesSKILL.mdcore · 11,416 bytesSKILL.mdcore · 11,416 bytesSKILL.mdcore · 11,416 bytesSKILL.mdcore · 11,416 bytesSKILL.mdcore · 11,416 byteseval_report_elastic-net-feature-selection_result.jsonasset · 12,497 bytesreferences/algorithm.mddoc · 3,583 bytesreferences/cli-guide.mddoc · 5,229 bytesreferences/troubleshooting.mddoc · 4,546 bytesscripts/functions.Rasset · 3,772 bytesscripts/io.Rasset · 3,239 bytesscripts/main.Rruns · 4,429 bytesscripts/modeling.Rasset · 2,976 bytesscripts/output.Rasset · 2,267 bytesscripts/run_analysis.Rasset · 2,478 bytesscripts/utils.Rasset · 3,542 bytesscripts/validation.Rasset · 2,384 bytestests/data/expression_matrix.csvasset · 2,609 bytestests/data/genes.csvasset · 44 bytestests/data/groups.csvasset · 326 bytestests/run_tests.Rasset · 771 bytestests/testthat.Rasset · 887 bytestests/testthat/helper-load-scripts.Rasset · 519 bytestests/testthat/setup.Rasset · 249 bytestests/testthat/test-elastic-net.Rasset · 10,036 bytes
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# Elastic Net Feature Selection
## 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.
## Out of Scope
- Survival or Cox modeling
- Multiclass outcomes
- Single-cell data
- Non-expression tables