Genes, Vol. 8, Pages 201: Mutation Clusters from Cancer Exome

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 http://ift.tt/2kL5GAi) 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.

from # All Medicine by Alexandros G. Sfakianakis via Alexandros G.Sfakianakis on Inoreader http://ift.tt/2vzifmY
via IFTTT