title: BagReg: Protein inference through machine learning.
abstract: Protein inference from the identified peptides is of primary importance in the shotgun proteomics. The target of protein inference is to identify whether each candidate protein is truly present in the sample. To date, many computational methods have been proposed to solve this problem. However, there is still no method that can fully utilize the information hidden in the input data. In this article, we propose a learning-based method named BagReg for protein inference. The method firstly artificially extracts five features from the input data, and then chooses each feature as the class feature to separately build models to predict the presence probabilities of proteins. Finally, the weak results from five prediction models are aggregated to obtain the final result. We test our method on six public available data sets. The experimental results show that our method is superior to the state-of-the-art protein inference algorithms.
authors.affiliation: School of Software, Dalian University of Technology, Dalian, China.
authors.forename: Can
authors.initials: C
authors.lastname: Zhao
authors.affiliation: Baidu.com, Inc., No. 10, Shangdi 10th Street, Haidian District, Beijing, China.
authors.forename: Dao
authors.initials: D
authors.lastname: Liu
authors.affiliation: School of Software, Dalian University of Technology, Dalian, China.
authors.forename: Ben
authors.initials: B
authors.lastname: Teng
authors.affiliation: School of Software, Dalian University of Technology, Dalian, China. Electronic address: zyhe@dlut.edu.cn.
authors.forename: Zengyou
authors.initials: Z
authors.lastname: He
mesh_terms: D019295:Computational Biology; D030562:Databases, Protein; D000069550:Machine Learning; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins
keywords: Machine learning; Protein identification; Protein inference; Shotgun proteomics