FEATURE SELECTION AND CLASSIFICATION OF PRO-TOF DATA BASED ON SOFT INFORMATION

摘要：

In this paper, we introduce a feature selection and classification method for prOTOF Mass Spectrometry (MS) data profiles of diseased and healthy patients. The method is based on a special statistical measure, which quantifies the probability of the existence of peptidepeaks. A special ranking score that is based on the statistical measure is used for selecting features that can best distinguish diseased and healthy data profiles. Based on the selected features, we applied a variety of classification algorithms and the results are compared with that of a method which selects features only based on peak heights. The results show a significant improvement in classification error rate with our proposed method.

作者: LIN ZHANG JIAN-QIU ZHANG XIAO-BO ZHOU HONG-HUI WANG YU-FEI HUANG HUI LIU STEPHEN WONG

作者单位: School of Information and Electric Engineering, China University of Mining and Technology, Xuzhou, P Dept.of Electrical and Comp.Eng., Univ.of Texas at San Antonio, San Antonio,TX 78249 Texas Methodist Hospital Research Institute, Houston, TX 77030 Critical Care Medicine Dept., Clinical Center, NIH, Bethesda, MD 20892 Dept.Of Electrical and Comp.Eng., Univ.Of Texas at San Antonio, San Antonio,TX 78249 Dept.Of Bioengi

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 4018-4023

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）