Feature selection for tandem mass spectrum quality assessment via sparse logistical regression

摘要：

Machine learning algorithms are widely used for quality assessment of tandem mass spectra based on a number of features. However, it is still unclear which features are most relevant to the quality of tandem mass spectra. In this paper, a sparse logistical regression method is proposed for selecting the most relevant features from those features found in the literature. To investigate the performance of the proposed method, experiments are conducted on two datasets. The results show the sparse logistical regression model can effectively select a small number of highly relevant features for tandem mass spectrum quality assessment.

关键词： feature selection sparse logistical regression mass spectrum quality assessment

作者: Jiarui Ding Fang-Xiang Wu

作者单位: Department of Mechanical Engineering University of Saskatchewan,Saskatoon,SK Canada,S7N 5A9 Department of Mechanical Engineering University of Saskatchewan,Saskatoon,SK Canada,S7N 5A9 Division

会议类型: 国际会议

会议名称: The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 1-4

在线出版日期: 2009-06-11（万方平台首次上网日期，不代表论文的发表时间）