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This paper presents a hierarchical bayesian factor model specifically designed to model the known correlation structure of both peptides and proteins in unbiased, label free proteomics. The model utilizes partial identification information from peptide sequencing and database lookup as well as observed correlation in the data set in order to appropriately compress features into metaproteins and to...
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