[1]ZHAO Can,DUAN Qiong,HE Zengyou.Protein inference method based on probabilistic graphical model[J].CAAI Transactions on Intelligent Systems,2016,11(3):376-383.[doi:10.11992/tis.201603051]
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Protein inference method based on probabilistic graphical model
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
11
Number of periods:
2016 3
Page number:
376-383
Column:
学术论文—脑认知基础
Public date:
2016-06-25
- Title:
- Protein inference method based on probabilistic graphical model
- CLC:
- TP393
- DOI:
- 10.11992/tis.201603051
- Abstract:
- Proteomics is an emerging discipline that focuses on the large-scale study of proteins expressed inan organism. An explicit goal of proteomics is the prompt and accurate identification of all proteins in a cell or tissue. Generally, protein identification can be divided into two parts: peptide identification and protein inference. In peptide identification, the peptide sequence is identified from raw tandem mass spectrometry , while the goal of protein inference is to identify which of these identified proteins is truly present in the sample. Because of the inherent uncertainty of MS data and the complexity of the proteome, there are several challenges in protein identification. In this article, we propose a novel method based on the probabilistic graphical model (PGMPi) that introduces the influence of tandem mass spectrometry. This method transforms the protein inference problem into a probabilistic graphical model problem to be solved, in which the maximum posteriori probabilities of proteins are identified in order to identify the protein set that is actually present in the sample. PGMPi can not only achieve efficient performance in terms of identification, but also introduces only one parameter, which ensures the algorithm’s stability. The experimental results demonstrate that our method is superior to existing state-of-the-art protein inference algorithms.
- References:
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