Statistical Analysis of Mascot Peptide Identification with Active Logistic Regression

摘要：

We apply active learning and logistic regression to perform statistical analysis of Mascot peptide identification. Uncertainty sampling is used to select examples for labeling, and selected examples are labeled with reference data as the oracle. In each iteration of active learning, the penalized Newton-Raphson method is used to solve the logistic regression model. By testing the method on two datasets with known validity, the results have demonstrated that the proposed method can assign accurate probabilities to Mascot peptide identifications and have a high discrimination power to separate correct and incorrect peptide identifications. By use of active learning, superior classifiers have been achieved with a significantly reduced training dataset.

作者: Jinhong Shi Wenjun Lin1 Fang-Xiang Wu

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

会议类型: 国际会议

会议名称: The 4th International Conference on Bioinformatics and Biomedical Engineering(第四届IEEE生物信息与生物医学工程国际会议 iCBBE 2010)

会议地点: 成都

会议语种:英文

页码: 1-4

在线出版日期: 2010-06-18（万方平台首次上网日期，不代表论文的发表时间）