*K-means and cluster models for cancer signatures.

Citation data:

Biomolecular detection and quantification, ISSN: 2214-7535, Vol: 13, Page: 7-31

Publication Year:
2017
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SSRN
SSRN Id:
2802753
DOI:
10.1016/j.bdq.2017.07.001; 10.2139/ssrn.2802753
PMID:
29021969
Author(s):
Kakushadze, Zura ; Yu, Willie
Publisher(s):
Elsevier BV
Tags:
Biochemistry, Genetics and Molecular Biology; industry classification; clustering; cluster numbers; machine learning; statistical risk models; industry risk factors; optimization; regression; mean-reversion; correlation matrix; factor loadings; principal components; hierarchical agglomerative clustering; k-means; statistical methods; multilevel
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article description
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.