ArticleBiomolecular detection and quantification2017
*K-means and cluster models for cancer signatures.
Article in Biomolecular detection and quantification, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Biomarker identification of triple negative breast cancer subtypes using machine learning.NPJ systems biology and applications · 2026Article
- Impact of CD68, CD4, TNF-α, and COX-2 expression on disease-specific survival in Brazilian patients with OSCC.Brazilian oral research · 2025Article
- Reliable epithelial-mesenchymal transition biomarkers for colorectal cancer detection.Biomarkers in medicine · 2022Article
- Framework for classification of cancer gene expression data using Bayesian hyper-parameter optimization.Medical & biological engineering & computing · 2021Article
- A tumor microenvironment-specific gene expression signature predicts chemotherapy resistance in colorectal cancer patients.NPJ precision oncology · 2021Article
- lncRNA Profiles Enable Prognosis Prediction and Subtyping for Esophageal Squamous Cell Carcinoma.Frontiers in cell and developmental biology · 2021Article
- Machine Learning in Oncology: Methods, Applications, and Challenges.JCO clinical cancer informatics · 2020Article
- Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study.International journal of molecular sciences · 2020Article
- Mutation Clusters from Cancer Exome.Genes · 2017Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present *K-means clustering algorithm and source code by expanding statistical clustering methods applied in https://ssrn.com/abstract=2802753 to quantitative finance. *K-means is statistically deterministic without specifying initial centers, etc. We apply *K-means to extracting cancer signatures from genome data without using nonnegative matrix factorization (NMF). *K-means' computational cost is a fraction of NMF's. Using 1389 published samples for 14 cancer types, we find that 3 cancers (liver cancer, lung cancer and renal cell carcinoma) stand out and do not have cluster-like structures. Two clusters have especially high within-cluster correlations with 11 other cancers indicating common underlying structures. Our approach opens a novel avenue for studying such structures. *K-means is universal and can be applied in other fields. We discuss some potential applications in quantitative finance.
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Registered trials
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