Analysis of Proteomic Mass Spectra based on Multivariate Feature Fusion Visualization
Protein mass spectrometry (MS) pattern recognition has recently emerged as a new method for cancer diagnosis. Application of proteomic mass spectra coupled with pattern classification techniques to discover novel biomarkers has been successfully used for the predictive diagnoses of several cancer diseases. However, the extraction of good features that can represent the identities of different classes plays the frontal critical factor for effective classification. In addition, another major problem is how to effectively handle a large number of features. This paper address these two frontal issues for MS classification. We propose a method based on graphical multivariate feature fusion and use it to offer a visual representation of high dimensional data. The graphical processing method we propose, relies on using a multilayered structure of feature fusion which produces as output of the lower dimensional representation. We implement feature fusion by combining method of feature selection and feature extraction. The proposed methodology was tested using public MS-based cancer datasets and the results are promising.
Hui Meng Wenxue Hong Jialin Song Liqiang Wang
College of Electrical Engineering Yanshan University Qinhuangdao, Hebei Province, China College of Automotive and Energy Engineering Yanshan University Qinhuangdao, Hebei Province, China
国际会议
上海
英文
672-675
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）