title: Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data.
abstract: BACKGROUND
  Recent 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.
  
  
  RESULTS
  We 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.
  
  
  CONCLUSIONS
  The proposed method is suitable for analyzing high-throughput proteomics data.
authors.affiliation: Department of Chemistry, Tongji University, Shanghai, 200092, China.
authors.forename: Kai-Lin
authors.initials: KL
authors.lastname: Tang
authors.forename: Tong-Hua
authors.initials: TH
authors.lastname: Li
authors.forename: Wen-Wei
authors.initials: WW
authors.lastname: Xiong
authors.forename: Kai
authors.initials: K
authors.lastname: Chen
mesh_terms: D014408:Biomarkers, Tumor / Q000032:analysis; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D010051:Ovarian Neoplasms / Q000145:classification*; D040901:Proteomics / Q000379:methods*; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*
chemical_list: D014408:Biomarkers, Tumor