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

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 ttesting, 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 c...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Kai-Lin Tang, Tong-Hua Li, Wen-Wei Xiong, Kai Chen
• Keywords: BMCBI 2010, Cancer, SELDI-TOF, SELDI-TOF Data