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A Bayesian Approach to Protein Inference Problem in Shotgun Proteomics.

, , , , , and . J. Comput. Biol., 16 (8): 1183-1193 (2009)

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A computational approach toward label-free protein quantification using predicted peptide detectability., , , , , , , and . ISMB (Supplement of Bioinformatics), page 481-488. (2006)Fast and accurate identification of semi-tryptic peptides in shotgun proteomics., , , , , , , , , and . Bioinform., 24 (1): 102-109 (2008)Advancement in Protein Inference from Shotgun Proteomics Using Peptide Detectability., , , , , and . Pacific Symposium on Biocomputing, page 409-420. World Scientific, (2007)A Machine Learning Approach to Predicting Peptide Fragmentation Spectra., , , , and . Pacific Symposium on Biocomputing, page 219-230. World Scientific, (2006)A Bayesian Approach to Protein Inference Problem in Shotgun Proteomics., , , , , and . J. Comput. Biol., 16 (8): 1183-1193 (2009)Improving phosphopeptide identification in shotgun proteomics by supervised filtering of peptide-spectrum matches., , , and . BCB, page 316. ACM, (2013)A Bayesian Approach to Protein Inference Problem in Shotgun Proteomics., , , , , and . RECOMB, volume 4955 of Lecture Notes in Computer Science, page 167-180. Springer, (2008)A maximum-likelihood approach to absolute protein quantification in mass spectrometry., , , , , , and . BCB, page 296-305. ACM, (2015)