Comprehensive Evaluation of Multi-Omics Clustering Algorithms for Cancer Molecular Subtyping.

ABSTRACT: As a highly heterogeneous and complex disease, the identification of cancer's molecular subtypes is crucial for accurate diagnosis and personalized treatment. The integration of multi-omics data enables a comprehensive interpretation of the molecular characteristics of cancer at various biological levels. In recent years, an increasing number of multi-omics clustering algorithms for cancer molecular subtyping have been proposed. However, the absence of a definitive gold standard makes it challenging to evaluate and compare these methods effectively. In this study, we developed a general framework for the comprehensive evaluation of multi-omics clustering algorithms and introduced an innovative metric, the accuracy-weighted average index, which simultaneously considers both clustering perfo

SUBMITTER: Wang J 

PROVIDER: S-EPMC11816650 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature