Publication Date:
2017-08-16
Description:
Genes, Vol. 8, Pages 201: Mutation Clusters from Cancer Exome Genes doi: 10.3390/genes8080201 Authors: Zura Kakushadze Willie Yu We apply our statistically deterministic machine learning/clustering algorithm *K-means (recently developed in https://ssrn.com/abstract=2908286) to 10,656 published exome samples for 32 cancer types. A majority of cancer types exhibit a mutation clustering structure. Our results are in-sample stable. They are also out-of-sample stable when applied to 1389 published genome samples across 14 cancer types. In contrast, we find in- and out-of-sample instabilities in cancer signatures extracted from exome samples via nonnegative matrix factorization (NMF), a computationally-costly and non-deterministic method. Extracting stable mutation structures from exome data could have important implications for speed and cost, which are critical for early-stage cancer diagnostics, such as novel blood-test methods currently in development.
Electronic ISSN:
2073-4425
Topics:
Biology
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