Matthew The

Cited by

	All	Since 2016
Citations	194	193
h-index	7	7
i10-index	5	5

2016201720182019202020213 10 34 33 60 52

Public access

View all

7 articles

0 articles

available

not available

Based on funding mandates

Co-authors

Lukas KällProfessor, KTH Royal Institute of Technology, Science for Life LaboratoryVerified email at kth.se
William Stafford NobleProfessor of Genome Sciences, University of WashingtonVerified email at uw.edu
Michael MacCossProfessor of Genome Sciences, University of WashingtonVerified email at uw.edu
Yasset Perez-RiverolTeam Coordinator, Proteomics Resources, EMBL-EBI, UKVerified email at ebi.ac.uk
Michael R. HoopmannInstitute for Systems BiologyVerified email at systemsbiology.org
Magnus PalmbladLeiden University Medical CenterVerified email at lumc.nl
Samuel H PayneAssociate Professor, Brigham Young UniversityVerified email at byu.edu
Fredrik EdforsThe Royal Institute of Technology (KTH)Verified email at scilifelab.se
Heydar Maboudi AfkhamKTHVerified email at kth.se
Juan Antonio VizcainoProteomics Team Leader, EMBL-EBIVerified email at ebi.ac.uk
Johannes GrissPostDoc, EMBL-European Biofinformatics InstitueVerified email at ebi.ac.uk
Ryan WilsonUniversity of WashingtonVerified email at uw.edu
Hyungwon ChoiNational University of SingaporeVerified email at nus.edu.sg
Henry LamHong Kong University of Science and Technology OR Massachusetts Institute of Technology OR InstituteVerified email at ust.hk
Joon-Yong LeeScientist, BSD, PNNLVerified email at pnnl.gov
John T HalloranUC DavisVerified email at ucdavis.edu

Matthew The

TU Munich

Verified email at tum.de - Homepage

Bioinformatics Machine learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	98	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	25	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	19	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	11	2018
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	10	2020
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	9	2017
Integrated identification and quantification error probabilities for shotgun proteomics M The, L Käll Molecular & Cellular Proteomics 18 (3), 561-570, 2019	7	2019
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	4	2019
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	4	2018
Response to “comparison and Evaluation of Clustering Algorithms for Tandem Mass Spectra” J Griss, Y Perez-Riverol, M The, L Käll, JA Vizcaino Journal of proteome research 17 (5), 1993-1996, 2018	4	2018
The one-carbon pool controls mitochondrial energy metabolism via complex I and iron-sulfur clusters FA Schober, D Moore, I Atanassov, MF Moedas, P Clemente, Á Végvári, ... Science Advances 7 (8), eabf0717, 2021	3	2021
Identification of 7 000–9 000 Proteins from Cell Lines and Tissues by Single-Shot Microflow LC–MS/MS Y Bian, M The, P Giansanti, J Mergner, R Zheng, M Wilhelm, A Boychenko, ... Analytical chemistry, 2021		2021
Triqler for MaxQuant: Enhancing Results from MaxQuant by Bayesian Error Propagation and Integration M The, L Käll Journal of proteome research 20 (4), 2062-2068, 2021		2021
Integrating identification and quantification uncertainty for differential protein abundance analysis with Triqler M The, L Käll bioRxiv, 2020		2020
Statistical and machine learning methods to analyze large-scale mass spectrometry data M The KTH Royal Institute of Technology, Stockholm, Sweden., 2018		2018

The system can't perform the operation now. Try again later.

Articles 1–15

Citations per year

Duplicate citations

Merged citations

Add co-authorsCo-authors

Follow this author

Cited by

Co-authors