Neighborhood Rough Set Reduction-Based Gene Selection and Prioritization for Gene Expression Profile Analysis and Molecular Cancer Classification

被引:10
|
作者
Hou, Mei-Ling [1 ,2 ]
Wang, Shu-Lin [1 ,3 ]
Li, Xue-Ling [1 ]
Lei, Ying-Ke [1 ,4 ]
机构
[1] Chinese Acad Sci, Hefei Inst Intelligent Machines, Intelligent Comp Lab, Hefei 230031, Anhui, Peoples R China
[2] Univ Sci & Technol China, Dept Biol, Hefei 230027, Anhui, Peoples R China
[3] Hunan Univ, Sch Comp & Commun, Changsha 410082, Hunan, Peoples R China
[4] Inst Elect Engn, Dept Informat, Hefei 230037, Peoples R China
来源
JOURNAL OF BIOMEDICINE AND BIOTECHNOLOGY | 2010年
基金
中国博士后科学基金; 美国国家科学基金会;
关键词
BREAST-CANCER; PROSTATE-CANCER; PROGRESSION; PROGNOSIS; DISCOVERY; MARKERS;
D O I
10.1155/2010/726413
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Selection of reliable cancer biomarkers is crucial for gene expression profile-based precise diagnosis of cancer type and successful treatment. However, current studies are confronted with overfitting and dimensionality curse in tumor classification and false positives in the identification of cancer biomarkers. Here, we developed a novel gene-ranking method based on neighborhood rough set reduction for molecular cancer classification based on gene expression profile. Comparison with other methods such as PAM, ClaNC, Kruskal-Wallis rank sum test, and Relief-F, our method shows that only few top-ranked genes could achieve higher tumor classification accuracy. Moreover, although the selected genes are not typical of known oncogenes, they are found to play a crucial role in the occurrence of tumor through searching the scientific literature and analyzing protein interaction partners, which may be used as candidate cancer biomarkers.
引用
收藏
页数:12
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