Automated sparse feature selection in high-dimensional proteomics data via 1-bit compressed sensing and K-Medoids clustering

Automated sparse feature selection in high-dimensional proteomics data via 1-bit compressed sensing and K-Medoids clustering

Abstract Background High-dimensional proteomics data present significant challenges in biomarker discovery due to technical noise, feature redundancy, and multicollinearity. Current feature selection methods, including filter, wrapper, and embedded approaches, struggle with stability, sparsity, and...

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Bibliographic Details
Main Authors: FuDong Wen, Yue Su, Dan Liu, YuPeng Wang, MeiNa Liu
Format: Article
Language:English
Published: BMC 2025-07-01
Series:BMC Bioinformatics
Subjects:
Feature selection
Classification
Compressed sensing
K-Medoids clustering
Proteomics
Online Access:https://doi.org/10.1186/s12859-025-06193-2
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