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Sparse representation based feature selection for mass spectrometry data

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9 Author(s)
Jiqing Ke ; Inst. of Biomed. Eng. & Instrum., Hangzhou Dianzi Univ., Hangzhou, China ; Lei zhu ; Bin Han ; Qi Dai
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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.

Published in:

Bioinformatics and Biomedicine Workshops (BIBMW), 2010 IEEE International Conference on

Date of Conference:

18-18 Dec. 2010

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