Publication Date:
2014-12-13
Description:
In this paper, we study Copy Number Variation (CNV) data.The underlying process generating CNV segments is generally assumed to be memory-less, giving rise to an exponential distribution of segment lengths. In this paper, we provide evidence from cancer patient data, which suggests that this generative model is too simplistic , and that segment lengths follow a power-law distribution instead . We conjecture a simple preferential attachment generative model that provides the basis for the observed power-law distribution. We then show how an existing statistical method for detecting cancer driver genes can be improved by incorporating the power-law distribution in the null model.
Print ISSN:
1545-5963
Electronic ISSN:
1557-9964
Topics:
Biology
,
Computer Science
Published by
Institute of Electrical and Electronics Engineers (IEEE)
on behalf of
The IEEE Computational Intelligence Society ; The IEEE Computer Society ; The IEEE Control Systems Society ; The IEEE Engineering in Medicine and Biology Society ; The Association for Computing Machinery.
