Zura Kakushadze

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We present a novel method for extracting cancer signatures by applying statistical risk models (http://ssrn.com/abstract=2732453) from quantitative finance to cancer genome data. Using 1389 whole genome sequenced samples from 14 cancers, we identify an " overall " mode of somatic mutational noise. We give a prescription for factoring out this noise and(More)
To my mother Ludmila (Mila) Kakushadze on the occasion of her upcoming birthday Abstract We propose a new index to quantify SSRN downloads. Unlike the SSRN downloads rank, which is based on the total number of an author's SSRN downloads, our index also reflects the author's productivity by taking into account the download numbers for the papers. Our index(More)
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 mutation clustering structure. Our results are in-sample stable. They are also out-of-sample stable when applied to 1,389(More)
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