Lukas Käll - Google Scholar Citations

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Citations	7628	4915
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Co-authors

William Stafford NobleProfessor of Genome Sciences, University of WashingtonVerified email at uw.edu
Erik SonnhammerStockholm Bioinformatics Centre, DBB, Stockholm University, Science for Life LaboratoryVerified email at scilifelab.se
Michael MacCossProfessor of Genome Sciences, University of WashingtonVerified email at uw.edu
Anders KroghDepartment of Biology, University of CopenhagenVerified email at binf.ku.dk
Viktor GranholmStockholm UniversityVerified email at scilifelab.se
John D. StoreyProfessor, Princeton UniversityVerified email at princeton.edu
Jason WestonFacebookVerified email at fb.com
Arne ElofssonScience for Life Laboratory and department of Biochemistry and Biophysics, Stockholm UniversityVerified email at bioinfo.se
Michael R. HoopmannInstitute for Systems BiologyVerified email at systemsbiology.org
Konstantinos TsirigosCopenhagen UniversityVerified email at cpr.ku.dk
Fredrik EdforsStanford, Department of GeneticsVerified email at scilifelab.se
Matthew TheKTH Royal Institute of TechnologyVerified email at kth.se
Mathias UhlénScience for Life Laboratory, KTH, StockholmVerified email at scilifelab.se
Nanjiang ShuDepartment of Biochemistry and Biophysics, Stockholm UniversityVerified email at scilifelab.se
Samuel H PayneAssociate Professor, Brigham Young UniversityVerified email at byu.edu
Sangtae KimIlluminaVerified email at illumina.com
Jeffrey A. BilmesProfessor of Electrical Engineering and Computer Science, University of Washington SeattleVerified email at uw.edu
Michael RiffleUniversity of WashingtonVerified email at uw.edu
Gunnar von HeijneStockholm UniversityVerified email at dbb.su.se
IngMarie NilssonSenior researcher, Stockholm UniversityVerified email at dbb.su.se

Lukas Käll

Professor, KTH Royal Institute of Technology, Science for Life Laboratory

Verified email at kth.se - Homepage

Computational Biology Proteomics Machine learning


Title	Cited by	Year
A combined transmembrane topology and signal peptide prediction method L Käll, A Krogh, ELL Sonnhammer Journal of molecular biology 338 (5), 1027-1036, 2004	1745	2004
Semi-supervised learning for peptide identification from shotgun proteomics datasets L Käll, JD Canterbury, J Weston, WS Noble, MJ MacCoss Nature methods 4 (11), 923, 2007	1113	2007
Advantages of combined transmembrane topology and signal peptide prediction—the Phobius web server L Käll, A Krogh, ELL Sonnhammer Nucleic acids research 35 (suppl_2), W429-W432, 2007	907	2007
Assigning significance to peptides identified by tandem mass spectrometry using decoy databases L Käll, JD Storey, MJ MacCoss, WS Noble Journal of proteome research 7 (01), 29-34, 2007	534	2007
The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides KD Tsirigos, C Peters, N Shu, L Käll, A Elofsson Nucleic acids research 43 (W1), W401-W407, 2015	301	2015
An HMM posterior decoder for sequence feature prediction that includes homology information L Käll, A Krogh, ELL Sonnhammer Bioinformatics 21 (suppl_1), i251-i257, 2005	269	2005
Posterior error probabilities and false discovery rates: two sides of the same coin L Käll, JD Storey, MJ MacCoss, WS Noble Journal of proteome research 7 (01), 40-44, 2007	233	2007
Transmembrane topology and signal peptide prediction using dynamic bayesian networks SM Reynolds, L Käll, ME Riffle, JA Bilmes, WS Noble PLoS computational biology 4 (11), e1000213, 2008	195	2008
Membrane topology of the Drosophila OR83b odorant receptor C Lundin, L Käll, SA Kreher, K Kapp, EL Sonnhammer, JR Carlson, ... FEBS letters 581 (29), 5601-5604, 2007	194	2007
Improvements to the percolator algorithm for Peptide identification from shotgun proteomics data sets M Spivak, J Weston, L Bottou, L Käll, WS Noble Journal of proteome research 8 (7), 3737-3745, 2009	175	2009
A general model of G protein‐coupled receptor sequences and its application to detect remote homologs M Wistrand, L Käll, ELL Sonnhammer Protein science 15 (3), 509-521, 2006	175	2006
qvality: non-parametric estimation of q-values and posterior error probabilities L Käll, JD Storey, WS Noble Bioinformatics 25 (7), 964-966, 2009	161*	2009
HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics RMM Branca, LM Orre, HJ Johansson, V Granholm, M Huss, ... Nature methods 11 (1), 59, 2014	160	2014
Rapid and accurate peptide identification from tandem mass spectra CY Park, AA Klammer, L Kall, MJ MacCoss, WS Noble Journal of proteome research 7 (7), 3022-3027, 2008	157	2008
Gene‐specific correlation of RNA and protein levels in human cells and tissues F Edfors, F Danielsson, BM Hallström, L Käll, E Lundberg, F Pontén, ... Molecular systems biology 12 (10), 2016	142	2016
Use of shotgun proteomics for the identification, confirmation, and correction of C. elegans gene annotations GE Merrihew, C Davis, B Ewing, G Williams, L Käll, BE Frewen, WS Noble, ... Genome research 18 (10), 1660-1669, 2008	95	2008
Reliability of transmembrane predictions in whole‐genome data L Käll, ELL Sonnhammer FEBS letters 532 (3), 415-418, 2002	78	2002
Peptide-centric proteome analysis: an alternative strategy for the analysis of tandem mass spectrometry data YS Ting, JD Egertson, SH Payne, S Kim, B MacLean, L Käll, R Aebersold, ... Molecular & Cellular Proteomics 14 (9), 2301-2307, 2015	69	2015
Training, selection, and robust calibration of retention time models for targeted proteomics L Moruz, D Tomazela, L Käll Journal of proteome research 9 (10), 5209-5216, 2010	68	2010
A novel transmembrane topology of presenilin based on reconciling experimental and computational evidence A Henricson, L Käll, ELL Sonnhammer The FEBS journal 272 (11), 2727-2733, 2005	66	2005

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