DocumentCode :
191067
Title :
A combinatorial algorithm to identify independent and recurrent copy number aberrations across cancer types
Author :
Hsin-Ta Wu ; Hajirasouliha, Iman ; Raphael, Benjamin J.
Author_Institution :
Dept. of Comput. Sci., Brown Univ., Providence, RI, USA
fYear :
2014
fDate :
2-4 June 2014
Firstpage :
1
Lastpage :
1
Abstract :
Somatic copy number aberrations (SCNAs) are frequent in cancer genomes, but many of these are random events that do not contribute to the cancer phenotype. A common strategy to distinguish functional, driver events from such random passenger events it to identify recurrent aberrations shared by multiple samples. However, the extensive variability in the length and position of SCNAs across samples makes the problem of identifying recurrent aberrations notoriously difficult.
Keywords :
bioinformatics; cancer; classification; combinatorial mathematics; data mining; genomics; medical computing; molecular biophysics; molecular configurations; SCNA length variability; SCNA position variability; cancer genomes; cancer phenotype; cancer types; combinatorial algorithm; functional driver event classification; independent copy number aberration identification; random SCNA events; random passenger event classification; recurrent copy number aberration identification; somatic copy number aberrations; Adaptation models; Bioinformatics; Cancer; Data models; Genomics; Heuristic algorithms; cancer; combinatorial algorithms; copy number aberrations;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Advances in Bio and Medical Sciences (ICCABS), 2014 IEEE 4th International Conference on
Conference_Location :
Miami, FL
Print_ISBN :
978-1-4799-5786-6
Type :
conf
DOI :
10.1109/ICCABS.2014.6863945
Filename :
6863945
Link To Document :
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