Show simple item record Tang, Haixu Sheng, Quanhu Radivojac, Predrag Li, Yixue Arnold, Randy J. Li, Yong Fuga 2011-12-22T01:27:38Z 2011-12-22T01:27:38Z 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 en
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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