Sparse representation based feature selection for mass spectrometry data

Mass spectrometry (MS) data has been widely analyzed for the detection of early stage cancers. Its potential for seeking proteomic biomarkers has received a great deal of attention in recent years. In the sparse representation classification (SRC) framework, a testing sample is represented as a sparse linear combination of training samples. The coefficient vector of representation is obtained by a ℓ¹-norm regularized least square method. Classification results are achieved by defining discriminant functions from the coefficient vector for each category. In this paper, a novel feature selection method based on SRC was proposed. To investigate its performance, the proposed methods was tested and evaluated on the ovarian cancer database OC-WCX2a and OC-WCX2b. The experimental results showed that SRC is efficient for tumor classification. Feature selection based on sparse representation (SRFS) can select highly predictive representative feature sets.