| SignalP 5.0 improves signal peptide predictions using deep neural networks JJ Almagro Armenteros, KD Tsirigos, CK Sønderby, TN Petersen, ... Nature biotechnology 37 (4), 420-423, 2019 | 3847 | 2019 |
| SignalP 6.0 predicts all five types of signal peptides using protein language models F Teufel, JJ Almagro Armenteros, AR Johansen, MH Gíslason, SI Pihl, ... Nature biotechnology 40 (7), 1023-1025, 2022 | 1374 | 2022 |
| 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 | 935 | 2015 |
| DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks J Hallgren, KD Tsirigos, MD Pedersen, JJ Almagro Armenteros, ... BioRxiv, 2022.04. 08.487609, 2022 | 626 | 2022 |
| DisProt 7.0: a major update of the database of disordered proteins D Piovesan, F Tabaro, I Mičetić, M Necci, F Quaglia, CJ Oldfield, ... Nucleic acids research 45 (D1), D219-D227, 2017 | 333 | 2017 |
| A reference map of potential determinants for the human serum metabolome N Bar, T Korem, O Weissbrod, D Zeevi, D Rothschild, S Leviatan, ... Nature 588 (7836), 135-140, 2020 | 331 | 2020 |
| Combined prediction of Tat and Sec signal peptides with hidden Markov models PG Bagos, EP Nikolaou, TD Liakopoulos, KD Tsirigos Bioinformatics 26 (22), 2811-2817, 2010 | 269 | 2010 |
| A brief history of protein sorting prediction H Nielsen, KD Tsirigos, S Brunak, G von Heijne The protein journal 38, 200-216, 2019 | 215 | 2019 |
| The next-generation Open Targets Platform: reimagined, redesigned, rebuilt D Ochoa, A Hercules, M Carmona, D Suveges, J Baker, C Malangone, ... Nucleic acids research 51 (D1), D1353-D1359, 2023 | 173 | 2023 |
| Prediction of lipoprotein signal peptides in Gram-positive bacteria with a Hidden Markov Model PG Bagos, KD Tsirigos, TD Liakopoulos, SJ Hamodrakas Journal of proteome research 7 (12), 5082-5093, 2008 | 142 | 2008 |
| DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation F Quaglia, B Mészáros, E Salladini, A Hatos, R Pancsa, LB Chemes, ... Nucleic acids research 50 (D1), D480-D487, 2022 | 137 | 2022 |
| Prediction of signal peptides in archaea PG Bagos, KD Tsirigos, SK Plessas, TD Liakopoulos, SJ Hamodrakas Protein Engineering, Design & Selection 22 (1), 27-35, 2009 | 106 | 2009 |
| PRED-TMBB2: improved topology prediction and detection of beta-barrel outer membrane proteins KD Tsirigos, A Elofsson, PG Bagos Bioinformatics 32 (17), i665-i671, 2016 | 88 | 2016 |
| Inclusion of dyad-repeat pattern improves topology prediction of transmembrane β-barrel proteins S Hayat, C Peters, N Shu, KD Tsirigos, A Elofsson Bioinformatics 32 (10), 1571-1573, 2016 | 87 | 2016 |
| OMPdb: a database of β-barrel outer membrane proteins from Gram-negative bacteria KD Tsirigos, PG Bagos, SJ Hamodrakas Nucleic acids research 39 (suppl_1), D324-D331, 2010 | 70 | 2010 |
| Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study A Wesolowska-Andersen, CA Brorsson, R Bizzotto, A Mari, A Tura, ... Cell Reports Medicine 3 (1), 2022 | 64 | 2022 |
| Improved topology prediction using the terminal hydrophobic helices rule C Peters, KD Tsirigos, N Shu, A Elofsson Bioinformatics 32 (8), 1158-1162, 2016 | 58 | 2016 |
| Pharmacogenomics of GLP-1 receptor agonists: a genome-wide analysis of observational data and large randomised controlled trials AY Dawed, A Mari, A Brown, TJ McDonald, L Li, S Wang, MG Hong, ... The Lancet Diabetes & Endocrinology 11 (1), 33-41, 2023 | 45 | 2023 |
| Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models RL Allesøe, AT Lundgaard, R Hernández Medina, A Aguayo-Orozco, ... Nature biotechnology 41 (3), 399-408, 2023 | 42 | 2023 |
| A guideline to proteome‐wide α‐helical membrane protein topology predictions KD Tsirigos, A Hennerdal, L Käll, A Elofsson Proteomics 12 (14), 2282-2294, 2012 | 41 | 2012 |
