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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Detalhes bibliográficos

Principais autores:	FuDong Wen, Yue Su, Dan Liu, YuPeng Wang, MeiNa Liu
Formato:	Artigo
Idioma:	Inglês
Publicado em:	BMC 2025-07-01
coleção:	BMC Bioinformatics
Assuntos:	Feature selection Classification Compressed sensing K-Medoids clustering Proteomics
Acesso em linha:	https://doi.org/10.1186/s12859-025-06193-2
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