Inferring progression models for CGH data | Zendy

Jun Liu | Zendy; Nirmalya Bandyopadhyay | Zendy; Sanjay Ranka | Zendy; Michael Baudis | Zendy; Tamer Kahveci | Zendy

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Inferring progression models for CGH data

Author(s) -

Jun Liu,

Nirmalya Bandyopadhyay,

Sanjay Ranka,

Michael Baudis,

Tamer Kahveci

Publication year - 2009

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btp365

Subject(s) - computational biology , comparative genomic hybridization , cancer , directed acyclic graph , biology , population , genome , computer science , genetics , gene , medicine , algorithm , environmental health

One of the mutational processes that has been monitored genome-wide is the occurrence of regional DNA copy number alterations (CNAs), which may lead to deletion or over-expression of tumor suppressors or oncogenes, respectively. Understanding the relationship between CNAs and different cancer types is a fundamental problem in cancer studies.

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