Predictive value of recurrent DNA copy number variations

Abdullah K. Alqallaf, Ahmed H. Tewfik, Scott Brian Selleck, Rebecca L. Johnson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Recurrent copy number variations across multiple samples are increasingly used to identify the genes and the genomic locations that are statistically and biologically significant and correlated with certain diseases. In this paper, we evaluate the predictive power of copy number variations for detecting autism. We consider both recurrent copy number variations at one location and correlated recurrent copy number variations at multiple locations. In each case, we compare the ability of k-means and Fuzzy c-means algorithms to correctly classify autistic samples. Finally, we apply our proposed techniques on 51 samples of 25 apparently healthy and 26 autistic children.

Original languageEnglish (US)
Title of host publicationGENSIPS'08 - 6th IEEE International Workshop on Genomic Signal Processing and Statistics
DOIs
StatePublished - Sep 17 2008
Event6th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'08 - Phoenix, AZ, United States
Duration: Jun 8 2008Jun 10 2008

Publication series

NameGENSIPS'08 - 6th IEEE International Workshop on Genomic Signal Processing and Statistics

Other

Other6th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'08
Country/TerritoryUnited States
CityPhoenix, AZ
Period6/8/086/10/08

All Science Journal Classification (ASJC) codes

  • Genetics
  • Signal Processing
  • Electrical and Electronic Engineering
  • Statistics, Probability and Uncertainty

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