Heterogeneity-Preserving Discriminative Feature Selection for Disease-Specific Subtype Discovery.

ABSTRACT: The identification of disease-specific subtypes can provide valuable insights into disease progression and potential individualized therapies, important aspects of precision medicine given the complex nature of disease heterogeneity. The advent of high-throughput technologies has enabled the generation and analysis of various molecular data types, such as single-cell RNA-seq, proteomic, and imaging datasets, on a large scale. While these datasets offer opportunities for subtype discovery, they also pose challenges in finding subtype signatures due to their high dimensionality. Feature selection, a key step in the machine learning pipeline, involves selecting signatures that reduce feature size for more efficient downstream computational analysis. Although many existing methods focus on sel

SUBMITTER: Basher ARMA 

PROVIDER: S-EPMC10769187 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature