Mutation Clusters from Cancer Exome
Quantigic® Solutions LLC, 1127 High Ridge Road #135, Stamford, CT 06905, USA
Business School & School of Physics 240, Free University of Tbilisi, David Agmashenebeli Alley, 0159 Tbilisi, Georgia
Centre for Computational Biology, Duke-NUS Medical School, 8 College Road, Singapore 169857
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Received: 19 June 2017 / Revised: 26 July 2017 / Accepted: 7 August 2017 / Published: 15 August 2017
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.
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MDPI and ACS Style
Kakushadze, Z.; Yu, W. Mutation Clusters from Cancer Exome. Genes 2017, 8, 201.
Kakushadze Z, Yu W. Mutation Clusters from Cancer Exome. Genes. 2017; 8(8):201.
Kakushadze, Zura; Yu, Willie. 2017. "Mutation Clusters from Cancer Exome." Genes 8, no. 8: 201.
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