ArticleHereditas2025
WMRCA + : a weighted majority rule-based clustering method for cancer subtype prediction using metabolic gene sets.
Article in Hereditas, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Trends and hotspots in research related to tumor immune escape: bibliometric analysis and future perspectives.Frontiers in immunology · 2025Pooled it
- Emerging hallmarks and the rise of complexities and heterogeneity of tumor.Biochemistry and biophysics reports · 2025Review
- Machine learning-driven classification and prognostic prediction of kidney renal clear cell carcinoma using APOBEC family expression signatures.Scientific reports · 2025Article
- Involvement ofFrontiers in cell and developmental biology · 2025Review
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Authors and funding
7 authors.
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Abstract
Accurate classification of cancer subtypes plays a pivotal role in advancing precision medicine. In this study, we introduce WMRCA + , a novel clustering approach based on a weighted majority rule that integrates multi-omics data and incorporates metabolic gene sets to robustly determine the optimal number of clusters for tumor subtype identification. WMRCA + evaluates clustering performance using ten internal metrics and offers comprehensive functionalities for data preprocessing and visualization. When applied to The Cancer Genome Atlas (TCGA) lung cancer dataset using lipid metabolism-related gene sets, WMRCA + outperformed widely used clustering algorithms-including iCluster, SNF, NMF, CC, and CNMF-achieving an AUC of 0.947. WMRCA + provides robust, interpretable, and biologically meaningful clustering results, offering a valuable tool for improving the accuracy of cancer subtype prediction. The WMRCA + R package is freely available at https://github.com/guojunliu7/WMRCA .
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