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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yongying Jiang Lei Zhu Bin Han Yaojia Wang Ying Xu |
| Copyright Year | 2010 |
| Description | Author affiliation: Institute of Biomedical Engineering & Instrument, Hangzhou Dianzi University, Hangzhou, China (Lei Zhu; Bin Han; Yaojia Wang; Ying Xu) || Institute of mechanical Engineering, Wenzhou University, Wenzhou, China (Yongying Jiang) |
| Abstract | 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. |
| File Size | 204594 |
| File Format | |
| ISBN | 9781424458219 |
| e-ISBN | 9781424458240 |
| DOI | 10.1109/ICFCC.2010.5497372 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-05-21 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Mass spectroscopy Classification algorithms Neoplasms Proteins Databases tumor classification feature extraction protein mass spectrum Proteomics Feature extraction Pattern analysis Cancer Testing sparse representation |
| Content Type | Text |
| Resource Type | Article |
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