BagReg: Protein inference through machine learning |
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Institution: | 1. School of Software, Dalian University of Technology, Dalian, China;2. Baidu.com, Inc., No. 10, Shangdi 10th Street, Haidian District, Beijing, China;1. School of Software, Central South University, Changsha 410075, China;2. School of Electronics and Computer Science, Zhejiang Wanli University, Ningbo 315100, China;3. School of Computer and Data Science, Henan University of Urban Construction, Pingdingshan 467000, China;4. School of Information Science and Engineering, Central South University, Changsha 410083, China |
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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. |
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Keywords: | Protein inference Machine learning Shotgun proteomics Protein identification |
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