ULDA-based heuristic feature selection method for proteomic profile analysis and biomarker discovery |
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Authors: | Mingjin Zhang Wenming Wang |
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Affiliation: | a Key Laboratory for Advanced Materials and Research Centre of Analysis Test, 130 Meilong Rd Shanghai 200237, PR China b Department of chemistry, Qinghai normal university, Xining, 810008, PR China |
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Abstract: | Uncorrelated linear discriminant analysis (ULDA)-based heuristic feature selection (ULDA-HFS) method was proposed for sample classification and feature extraction for SELDI-TOF MS ovarian cancer data. The ULDA-HFS method includes 4 steps: (1) noise reduction and normalization; (2) selection of discriminatory bins with CHI2 method; (3) peak detection and alignment for each selected bins; and (4) selection of several peaks as potential biomarkers by means of ULDA. As a result, 7 m/z locations were selected in this study; they were 245.3, 559.4, 565.6, 704.2, 717.2, 2667 and 4074.4. To evaluate the classification impression, PCA, PLS-DA and ULDA were performed for discriminant analysis and ULDA obtained the perfect separation. Finally, the 7 selected potential biomarkers were evaluated by ULDA, both sensitivity and specificity were 100%. The 7 m/z values obtained may provide clues for ovarian cancer biomarker discovery. Once the proteins were identified at these m/z locations, it can be used as specific protein for early detection and diagnosis for ovarian cancer. |
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Keywords: | Uncorrelated linear discriminant analysis (ULDA) ULDA-based heuristic feature selection (ULDA-HFS) Proteomics Feature selection Biomarker |
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