Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data

BMC Bioinformatics

Research Article

Wen-Wei Xiong¹  Kai Chen¹  Tong-Hua Li¹  Kai-Lin Tang² 

[1] Department of Chemistry, Tongji University, 200092, Shanghai, China;Shanghai Center for Bioinformation Technology, 200235, Shanghai, China;Department of Chemistry, Tongji University, 200092, Shanghai, China;

关键词: Feature Selection;    Kernel Function;    Statistical Moment;    Kernel Matrix;    Proteomics Data Analysis;   

DOI  :  10.1186/1471-2105-11-109

 received in 2009-09-22, accepted in 2010-02-27,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundRecent advances in proteomics technologies such as SELDI-TOF mass spectrometry has shown promise in the detection of early stage cancers. However, dimensionality reduction and classification are considerable challenges in statistical machine learning. We therefore propose a novel approach for dimensionality reduction and tested it using published high-resolution SELDI-TOF data for ovarian cancer.ResultsWe propose a method based on statistical moments to reduce feature dimensions. After refining and t-testing, SELDI-TOF data are divided into several intervals. Four statistical moments (mean, variance, skewness and kurtosis) are calculated for each interval and are used as representative variables. The high dimensionality of the data can thus be rapidly reduced. To improve efficiency and classification performance, the data are further used in kernel PLS models. The method achieved average sensitivity of 0.9950, specificity of 0.9916, accuracy of 0.9935 and a correlation coefficient of 0.9869 for 100 five-fold cross validations. Furthermore, only one control was misclassified in leave-one-out cross validation.ConclusionThe proposed method is suitable for analyzing high-throughput proteomics data.

【 授权许可】

Unknown   
© Tang et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.