Sparse representation based feature extraction of protein mass spectrometry data for cancer classification

Protein mass spectrometry has become a popular tool for cancer diagnosis. This article describes a novel proteomic pattern analysis algorithm for tumor classification using SELDI-TOF mass spectrometry. Different from the traditional pattern analysis methods, sparse representation accepts a new frame. Firstly the MS data is preprocessed. Secondly, the proposed method seeks the sparse representation of test sample on training sample set. Then 2-fold cross validation is performed to evaluate classification ability. The proposed method was tested and evaluated in the ovarian cancer database OC-WCX2a, OC-WCX2b, prostate cancer database PC-H4. The experimental results show the good performance of sparse representation method.