title: A bayesian approach to protein inference problem in shotgun proteomics.
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 rigorious probabilistic model for protein inference and provide practical algoritmic solutions to this problem. We used a complex synthetic protein mixture to test our method and obtained promising results.
authors: School of Informatics, Indiana University , Bloomington, IN 47408, USA.
authors: Yong Fuga
authors: YF
authors: Li
authors: Randy J
authors: RJ
authors: Arnold
authors: Yixue
authors: Y
authors: Li
authors: Predrag
authors: P
authors: Radivojac
authors: Quanhu
authors: Q
authors: Sheng
authors: Haixu
authors: H
authors: Tang
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D015233:Models, Statistical; D040901:Proteomics / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*