LOCC: a novel visualization and scoring of cutoffs for continuous variables
George Luo1, John J Letterio2,3,4
1Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, Ohio.
Biorxiv : the Preprint Server for Biology
|April 24, 2023
Summary
Luo's Optimization Categorization Curve (LOCC) is a new tool for selecting gene cutoffs in cancer research. LOCC effectively ranks genes by prognostic potential, outperforming traditional ROC curves.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Selecting significant gene cutoffs is crucial for prognostic modeling in cancer.
- Existing methods may not optimally identify the most impactful prognostic genes.
Conclusions:
- LOCC is a valuable novel visualization tool for selecting gene expression cutoffs in cancer research.
- The LOCC score offers a robust method for ranking genes based on prognostic potential.
- LOCC demonstrates advantages over ROC curves for prognostic modeling in genomic studies.
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