DocumentCode :
1809933
Title :
Workshop: Algorithms for discovery of mutated pathways in cancer
Author :
Vandin, Fabio ; Upfal, Eli ; Raphael, Benjamin J.
Author_Institution :
Dept. of Comput. Sci., Brown Univ., Providence, RI, USA
fYear :
2012
fDate :
23-25 Feb. 2012
Firstpage :
1
Lastpage :
1
Abstract :
Markov chain Monte Carlo algorithm is applied to several cancer types including glioblastoma multiforme (GBM), lung adenocarcinoma, and ovarian carcinoma (OV). HotNet identifies significant subnetworks that are part of well-known cancer pathways as well as novel subnetworks. Among the most significant subnetworks identified in OV data is the Notch signaling pathway. HotNet is used to identify mutated pathways associated with patient survival. In the TCGA OV data, 9 subnetworks containing genes whose mutations are associated with survival is discovered. Genes in 4 of these subnetworks overlap pathways known to be associated to survival, including focal adhesion and cell adhesion pathways. In GBM and lung Dendrix finds significant sets of genes that are mutated in large subsets of patients and whose mutations are approximately exclusive, including genes in well known cancer pathways.
Keywords :
Markov processes; Monte Carlo methods; adhesion; cancer; cellular biophysics; genetics; GBM; HotNet; Markov chain Monte Carlo algorithm; Notch signaling pathway; cancer pathways; cell adhesion pathways; focal adhesion; genes; glioblastoma multiforme; lung Dendrix; lung adenocarcinoma; mutated pathways; ovarian carcinoma; patient survival; Adhesives; Algorithm design and analysis; Bioinformatics; Cancer; Genomics; Lungs; Monte Carlo methods; Markov chain Monte Carlo; cancer genomics; diffusion processes; driver pathways; networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Advances in Bio and Medical Sciences (ICCABS), 2012 IEEE 2nd International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4673-1320-9
Electronic_ISBN :
978-1-4673-1319-3
Type :
conf
DOI :
10.1109/ICCABS.2012.6182678
Filename :
6182678
Link To Document :
بازگشت