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Genes 2017, 8(8), 201; doi:10.3390/genes8080201

Mutation Clusters from Cancer Exome

1,2,†,* and 3
1
Quantigic® Solutions LLC, 1127 High Ridge Road #135, Stamford, CT 06905, USA
2
Business School & School of Physics 240, Free University of Tbilisi, David Agmashenebeli Alley, 0159 Tbilisi, Georgia
3
Centre for Computational Biology, Duke-NUS Medical School, 8 College Road, Singapore 169857
Disclaimer: This address is used by the corresponding author for no purpose other than to indicate his professional affiliation as is customary in publications. In particular, the contents of this paper are not intended as an investment, legal, tax or any other such advice and in no way represent the views of Quantigic® Solutions LLC, the website www.quantigic.com or any of their other affiliates.
*
Author to whom correspondence should be addressed.
Received: 19 June 2017 / Revised: 26 July 2017 / Accepted: 7 August 2017 / Published: 15 August 2017
(This article belongs to the Special Issue Integrative Genomics and Systems Medicine in Cancer)

Abstract

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. View Full-Text
Keywords: clustering; K-means; nonnegative matrix factorization; somatic mutation; cancer signatures; genome; exome; DNA; eRank; correlation; covariance; machine learning; sample; matrix; source code; quantitative finance; statistical risk model; industry classification clustering; K-means; nonnegative matrix factorization; somatic mutation; cancer signatures; genome; exome; DNA; eRank; correlation; covariance; machine learning; sample; matrix; source code; quantitative finance; statistical risk model; industry classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Kakushadze, Z.; Yu, W. Mutation Clusters from Cancer Exome. Genes 2017, 8, 201.

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