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Updated: Aug 30, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
A supervised take on dimensionality reduction via hybrid subset selection
Javad Rahimikollu1,2, Jishnu Das2
1CMU-Pitt Program in Computational Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Abstract:
Amouzgar et al. present HSS-LDA, a supervised dimensionality reduction approach for single-cell data that outperforms existing unsupervised techniques. They couple hybrid subset selection to linear discriminant analysis and identify interpretable linear combinations of predictors that best separate predefined biological groups.
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