Matthew The
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Fast and accurate protein false discovery rates on large-scale proteomics data sets with percolator 3.0
M The, MJ MacCoss, WS Noble, L Käll
Journal of The American Society for Mass Spectrometry 27 (11), 1719-1727, 2016
How to talk about protein‐level false discovery rates in shotgun proteomics
M The, A Tasnim, L Käll
Proteomics 16 (18), 2461-2469, 2016
MaRaCluster: A fragment rarity metric for clustering fragment spectra in shotgun proteomics
M The, L Käll
Journal of proteome research 15 (3), 713-720, 2016
A protein standard that emulates homology for the characterization of protein inference algorithms
M The, F Edfors, Y Perez-Riverol, SH Payne, MR Hoopmann, M Palmblad, ...
Journal of proteome research 17 (5), 1879-1886, 2018
Uncertainty estimation of predictions of peptides’ chromatographic retention times in shotgun proteomics
H Maboudi Afkham, X Qiu, M The, L Käll
Bioinformatics 33 (4), 508-513, 2017
Focus on the spectra that matter by clustering of quantification data in shotgun proteomics
M The, L Käll
Nature Communications 11 (1), 1-12, 2020
Integrated identification and quantification error probabilities for shotgun proteomics
M The, L Käll
Molecular & Cellular Proteomics 18 (3), 561-570, 2019
Response to “Comparison and Evaluation of Clustering Algorithms for Tandem Mass Spectra”
J Griss, Y Perez-Riverol, M The, L Käll, JA Vizcaíno
Journal of proteome research 17 (5), 1993-1996, 2018
ABRF Proteome Informatics Research Group (iPRG) 2016 Study: Inferring Proteoforms from Bottom-up Proteomics Data
JY Lee, H Choi, CM Colangelo, D Davis, MR Hoopmann, L Käll, H Lam, ...
Journal of biomolecular techniques: JBT 29 (2), 39, 2018
Speeding Up Percolator
JT Halloran, H Zhang, K Kara, C Renggli, M The, C Zhang, DM Rocke, ...
Journal of Proteome Research 18 (9), 3353-3359, 2019
Statistical and machine learning methods to analyze large-scale mass spectrometry data
M The
KTH Royal Institute of Technology, Stockholm, Sweden., 2018
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