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dc.contributor.author Tang, Haixu
dc.contributor.author Sheng, Quanhu
dc.contributor.author Radivojac, Predrag
dc.contributor.author Li, Yixue
dc.contributor.author Arnold, Randy J.
dc.contributor.author Li, Yong Fuga
dc.date.accessioned 2011-12-22T01:27:38Z
dc.date.available 2011-12-22T01:27:38Z
dc.date.issued 2009-08-18
dc.identifier.citation Yong Fuga Li, Randy J. Arnold, Yixue Li, Predrag Radivojac, Quanhu Sheng, and Haixu Tang. Journal of Computational Biology. August 2009, 16(8): 1183-1193. doi:10.1089/cmb.2009.0018. en
dc.identifier.uri http://www.liebertonline.com/doi/abs/10.1089/cmb.2009.0018 en
dc.identifier.uri http://hdl.handle.net/2022/14012
dc.description.abstract The protein inference problem represents a major challenge in shotgun proteomics. In this article, we describe a novel Bayesian approach to address this challenge by incorporating the predicted peptide detectabilities as the prior probabilities of peptide identification. We propose a rigorous probabilistic model for protein inference and provide practical algorithmic solutions to this problem. We used a complex synthetic protein mixture to test our method and obtained promising results. en
dc.language.iso en_US en
dc.publisher Mary Ann Liebert, Inc. en
dc.rights Copyright Mary Ann Liebert, Inc. en
dc.subject algorithms, alignment, combinatorial proteomics, computational molecular biology, databases, mass spectroscopy, proteins, sequence analysis en
dc.title A Bayesian Approach to Protein Inference Problem in Shotgun Proteomics en
dc.type Article en


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