BagReg: Protein inference through machine learning. (August 2015)

• Record Type: Journal Article
• Authors: Zhao, Can; Liu, Dao; Teng, Ben; He, Zengyou
• Is Part Of: Computational biology and chemistry. Volume 57(2015)
• Page Start: 12
• Page End: 20
• Publication Date: 2015-08
• Subjects: Protein inference -- Machine learning -- Shotgun proteomics -- Protein identification; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2015.02.009
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract : Graphical abstract: Abstract : Highlights: BagReg is a novel learning-based protein inference algorithm. BagReg can fully utilize the latent information in the input data. BagReg is competitive with the state-of-the-art protein inference algorithms. 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.