Identification of co-occurring insertions in cancer genomes using association analysis

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

Abstract

Collections of tumor genomes created by insertional mutagenesis experiments, e.g., the Retroviral Tagged Cancer Gene Database, can be analyzed to find connections between mutations of specific genes and cancer. Such connections are found by identifying the locations of insertions or groups of insertions that frequently occur in the collection of tumor genomes. Recent work has employed a kernel density approach to find such commonly occurring insertions or co-occurring pairs of insertions. Unfortunately, this approach is extremely compute intensive for pairs of insertions, and even more intractable for triples, etc. We present a novel approach that combines kernel density and association analysis (frequent pattern mining) techniques to efficiently find commonly co-occurring sets of insertions of any length. More generally, this approach can be used to find other commonly occurring features in collections of genomes.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
Pages494-499
Number of pages6
DOIs
StatePublished - 2010
Event2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010 - HongKong, China
Duration: Dec 18 2010Dec 21 2010

Publication series

Name2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010

Other

Other2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
CountryChina
CityHongKong
Period12/18/1012/21/10

Keywords

  • Cancer genomes
  • Frequent pattern mining
  • Kernel density

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