Semi-supervised learning for peptide identification from shotgun proteomics datasets
By Lukas Käll, J. Canterbury, Jason Aaron Edward Weston, William Stafford Noble, Michael J. MacCoss. Nature Methods, 2007
2,199 citations recorded by the source corpus.
Subfields: Molecular Biology, Spectroscopy
View Paper: https://www.semanticscholar.org/paper/0d7ee3f200a044b594606c9067bbcd1638ec32e3
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Alexey I. Nesvizhskii
- Steven P. Gygi
- Pavel A. Pevzner
- Sangtae Kim
- Joshua E. Elias
- R. Aebersold
- Tim J. P. Hubbard
- Markus Brosch
In Spectroscopy
- Alexey I. Nesvizhskii
- Steven P. Gygi
- Sangtae Kim
- Pavel A. Pevzner
- Joshua E. Elias
- R. Aebersold
- Tim J. P. Hubbard
- Markus Brosch
Citations in the corpus
- Target-Decoy Search Strategy for Mass Spectrometry-Based Proteomics (2010, background): “Evaluating and optimizing multiple candidate score threshold combinations can be tedious to perform manually; computational approaches for doing this have been described, however (22,23).”
- Data‐independent acquisition‐based SWATH‐MS for quantitative proteomics: a tutorial (2018, positive): “This goal can be achieved by using a semi-supervised learning algorithm (Kall et al , 2007) that converges iteratively towards the most optimal set of weighted individual score combinations to separate targets and decoys.”
- Universal database search tool for proteomics (2014, methodology): “This simple ‘dot-product’ scoring model contrasts with many other database search 2,4,27,28 and re-scoring 9,10 tools, using sophisticated scoring functions that often make it difficult to satisfy the third condition.”
- mProphet: automated data processing and statistical validation for large-scale SRM experiments (2011, neutral): “This is analogous to the situation in early shotgun proteomics, in which the tools to control the quality of the identification were developed long after the principal measurement method”
- Fast and Accurate Protein False Discovery Rates on Large-Scale Proteomics Data Sets with Percolator 3.0 (2016, methodology): “P ercolator [1] has played a prominent part in the analysis pipelines of shotgun proteomics experiments for the last decade, as a post-processor of the results from database search engines such as SEQUEST [2], MASCOT [3], X!”