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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...
Knowledge of structural classes is useful in understanding of folding patterns in proteins. Although numerous methods were proposed and achieved promising results in structural class prediction, some problems in using protein-sequence information have impeded the development. In this paper, a combined representation of protein-sequence information is proposed for prediction of protein structural class,...
Protein mass spectrometry has become a popular tool for cancer diagnosis. Feature selection and classification techniques play an important role in the identification of protein biomarkers. In this paper, based on the protein spectrum of cancer classification, an efficient combination of wavelet features and Recursive Null Space LDA algorithm for feature selection is proposed. Firstly, the multi-resolution...
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...
Early detection of cancer is crucial for successful treatments. High throughput and high resolution mass spectrometry are increasingly used for disease classification. In this paper a novel cancer classification method called Null space based linear discriminant analysis (NS-LDA) is proposed. NSLDA first extracts the first order derivative information of the mass spectrometry profiles. Based on the...
Differential in-gel electrophoresis (DIGE) technique has been used for differential protein analysis to improve the reproducibility of comparative 2D gel experiments. Because the sample size in 2D DIGE experiments is usually very small, the traditional statistical methods such as student t-test could not provide an accurate and reliable detection/identification of differentially expressed protein...
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