Exploration of ovarian cancer microarray data focusing on gene expression patterns relevant to survival using artificial neural networks

被引：0
|
作者：
Coveney, Clare [1 ]
Tong, Dong L. [1 ]
Boocock, David J. [1 ]
Rees, Robert C. [1 ]
Ball, Graham R. [1 ]
机构：
[1] Nottingham Trent Univ, John van Geest Canc Res Ctr, Sch Sci & Technol, Nottingham NG11 8N5, England
来源：
PROCEEDINGS IWBBIO 2014: INTERNATIONAL WORK-CONFERENCE ON BIOINFORMATICS AND BIOMEDICAL ENGINEERING, VOLS 1 AND 2 | 2014年
关键词：
artificial neural networks; ovarian cancer; survival time; gene microarray; PRELP; CELLS;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
A stage III ovarian cancer diagnosis yields a 22% 5-year survival rate, this applies to over half of the 7000 new cases diagnosed each year in the UK. Stratification of patients with this heterogeneous disease, based on active molecular pathways in their cancer, would aid a targeted treatment and improve prognosis. Hundreds of genes have been significantly associated with ovarian cancer, few have yet been verified. Exploration of published microarray data sets using Artificial Neural Networks confirmed the robustness of PRELP as a biomarker for survival time from stage III ovarian cancer, and generated a new panel of 44 genes that significantly predicted survival length of a blind validation set (p=0.00073).
引用
收藏
页码：116 / 123
页数：8
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