Inference of cancer progression from somatic mutation data

被引：1
|
作者：
Wu, Hao [1 ,2 ,3 ]
Gao, Lin [1 ]
Kasabov, Nikola [3 ]
机构：
[1] Xidian Univ, Sch Comp Sci & Technol, Xian, Shaanxi, Peoples R China
[2] Northwest A&F Univ, Coll Informat Engn, Yangling, Shaanxi, Peoples R China
[3] Auckland Univ Technol, Knowledge Engn & Discovery Res Inst, Auckland, New Zealand
来源：
IFAC PAPERSONLINE | 2015年 / 48卷 / 28期
关键词：
Cancer genome; Cancer progression; Network models; Dynamic problem; Driver mutation; DRIVER PATHWAYS; TUMOR PROGRESSION; TREE MODELS;
D O I：
10.1016/j.ifacol.2015.12.131
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Large-scale cancer genomics projects are providing a wealth of somatic mutation data. Therefore, one of the most challenging problems arising from the data is to infer the temporal order of somatic mutations. In the paper, we present a network-based method (NetInf) to infer cancer progression at the pathway level. We apply it to analyze somatic mutation data from real cancer studies. Experimental results show that these detected pathways overlap with known pathways, including RB, P53 signaling pathways. Our method reduces computational complexity and also provides new insights on the temporal order of somatic mutations at the pathway level. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码：234 / 238
页数：5
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