运用近邻传播聚类分析进行SELDI-TOF蛋白质谱特征选择

doi:10.3969/j.issn.0258-8021.2013.01.003

Abstract
Figure/Table
References (0)
Related Citation (2)

Download: PDF (773 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract To analysis high throughput and high resolution mass spectrometry data effectively and capture the cancer related protein feature from the mass spectrometry data, diagnosis called a feature selection based on affinity propagation clustering of mass spectrometry was proposed in this paper. Firstly, the ttest was used on mass spectrometry data, followed by feature selection based on affinity propagation clustering. Next, affinity propagtion clustering and NS-LDA was used for reducing dimensions and correlation. Thirdly, SVM-RFE was used to select the features. Finally, we used four classifiers to estimate the performance of the algorithm. The proposed method was tested and evaluated on the ovarian cancer database OC-WCX2a, OC-WCX2b, and breast cancer database BC-WCX2a. Classification was achieved 96.43 %, 99.66 % and 90.88 %, sensitivity was achieved 97.00 %, 100 % and 96.17 %, specificity was achieved 95.85 %, 99.08 % and 81.92 %, respectively. And 10 m/z features were selected for each dataset. The experimental results showed good performance of the method, and the method is expected to be used in cancer diagnosis.

Key words： mass spectrometry affinity propagation clustering feature selection biomarker

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	REN Yu Qiang¹LI Li Ming^1*CHAI Xin Yu¹REN Qiu Shi²YANG He Long^{1 &}

Cite this article:

REN Yu Qiang¹LI Li Ming^1*CHAI Xin Yu¹REN Qiu Shi²YANG He Long^{1 &. Feature Selection and Sample Classification for SELDITOF Mass Spectrometry Data Based on Affinity Propagation Clustering[J]. journal1, 2013, 32(1): 14-20.}

URL:

http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2013.01.003 OR http://cjbme.csbme.org/EN/Y2013/V32/I1/14

［1］Jemal A, Sieg el R, Ward E, et al. Cancer statistics ［J］. A Cancer Journal of Clinicians, 2011, (61): 212-236.
［2］Bruce A, Julian L, Martin R, et al. Molecular biology of the cell, fourth edition ［M］. New York: Garland Science, 2004.
［3］Shin H, Sheu B, Joseph M, et al. Guiltbyassociation feature selection: identifying biomakers from proteomic profiles ［J］. Biomed Inform, 2008, 41: 124-136.

［4］Somorjai RL, Dolenko B, Baumgartner R. Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions ［J］. Bioinformatics， 2003, 19(12): 1484-1491.
［5］Thomas A, Tourassi GD, Elmaghraby AS, et al. Data mining in proteomic mass spectrometry［J］. Clinical Proteomics, 2006, 2(1): 13-21.
［6］Hanczar B, Courtine M, Benis A, et al. Improving classification of microarray data using prototypebased feature selection［J］. SIGKDD Exploration, 2003, 5: 23-30.
［7］Wang Yuhang, Fillia Makedon, James Ford, et al. HykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data［J］. Bioinformatics, 2005, 218): 1530-1537.
［8］Yang Pengyi, Zhang Zili, Zhou Bingbing, et al. A clustering based hybrid system for biomaker selection and sample classification of mass spectrometry data［J］. Neurocomputing, 2010, 73(13-16): 2317-2331.
［9］Yang Pengyi, Zhang Zili. A clustering based hybrid system for mass spectrometry data analysis［J］. Pattern Recognition in Bioinformatics, Lecture Notes in Bioinformatics, 2008, 5265: 98-109.
［10］Huang Rui, Liu Qingshan, Lu Hanqing, et al. Solving the small sample size problem of LDA ［J］. Pattern Recognition, 2002, 3: 29-32.
［11］Dueck D, Frey BJ. Clustering by passing messages between data points［J］. Science, 2007, 315(5814): 972-976.
［12］李伟红，龚卫国，陈伟民，等. 基于SVMRFE的人脸特征选择方法［J］. 光电工程, 2006, 33(5): 113-117.
［13］王尧佳，祝磊，韩斌. 基于递归零空间线性判别分析算法的蛋白质质谱数据特征选择［J］. 航天医学与医学工程, 2010, 23(5): 324-328.
［14］孟辉，洪文学. 蛋白质组学质谱数据预处理技术综述［J］. 中国生物医学工程学报, 2009, 28(3): 469-475.