| Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes A Krogh, B Larsson, G Von Heijne, ELL Sonnhammer Journal of molecular biology 305 (3), 567-580, 2001 | 14359 | 2001 |
| Tissue-based map of the human proteome M Uhlén, L Fagerberg, BM Hallström, C Lindskog, P Oksvold, ... Science 347 (6220), 1260419, 2015 | 14317 | 2015 |
| SignalP 4.0: discriminating signal peptides from transmembrane regions TN Petersen, S Brunak, G Von Heijne, H Nielsen Nature methods 8 (10), 785-786, 2011 | 10195 | 2011 |
| Improved prediction of signal peptides: SignalP 3.0 JD Bendtsen, H Nielsen, G Von Heijne, S Brunak Journal of molecular biology 340 (4), 783-795, 2004 | 7991 | 2004 |
| Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. H Nielsen, J Engelbrecht, S Brunak, G Von Heijne Protein engineering 10 (1), 1-6, 1997 | 6798 | 1997 |
| A new method for predicting signal sequence cleavage sites G Von Heijne Nucleic acids research 14 (11), 4683-4690, 1986 | 5378 | 1986 |
| Predicting subcellular localization of proteins based on their N-terminal amino acid sequence O Emanuelsson, H Nielsen, S Brunak, G Von Heijne Journal of molecular biology 300 (4), 1005-1016, 2000 | 5208 | 2000 |
| Locating proteins in the cell using TargetP, SignalP and related tools O Emanuelsson, S Brunak, G Von Heijne, H Nielsen Nature protocols 2 (4), 953-971, 2007 | 3826 | 2007 |
| A hidden Markov model for predicting transmembrane helices in protein sequences. ELL Sonnhammer, G Von Heijne, A Krogh Ismb 6, 175-182, 1998 | 3311 | 1998 |
| 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 | 2797 | 2019 |
| Signal sequences: the limits of variation G Von Heijne Journal of molecular biology 184 (1), 99-105, 1985 | 2781 | 1985 |
| Patterns of amino acids near signal‐sequence cleavage sites G Von Heijne European journal of biochemistry 133 (1), 17-21, 1983 | 2676 | 1983 |
| SignalP 5.0 improves signal peptide predictions using deep neural networks JJA Armenteros, KD Tsirigos, CK Sønderby, TN Petersen, O Winther, ... Nature biotechnology 37, 420-423, 2019 | 2669 | 2019 |
| ChloroP, a neural network‐based method for predicting chloroplast transit peptides and their cleavage sites O Emanuelsson, H Nielsen, GV Heijne Protein Science 8 (5), 978-984, 1999 | 2169 | 1999 |
| Membrane protein structure prediction: hydrophobicity analysis and the positive-inside rule G Von Heijne Journal of molecular biology 225 (2), 487-494, 1992 | 2130 | 1992 |
| Genome‐wide analysis of integral membrane proteins from eubacterial, archaean, and eukaryotic organisms E Wallin, GV Heijne Protein Science 7 (4), 1029-1038, 1998 | 2113 | 1998 |
| 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 | 1485 | 2022 |
| The signal peptide G von Heijne The Journal of membrane biology 115, 195-201, 1990 | 1458 | 1990 |
| Domain structure of mitochondrial and chloroplast targeting peptides G von HEIJNE, J Steppuhn, RG Herrmann European Journal of Biochemistry 180 (3), 535-545, 1989 | 1397 | 1989 |
| Prediction of transmembrane alpha-helices in prokaryotic membrane proteins: the dense alignment surface method. M Cserzö, E Wallin, I Simon, G von Heijne, A Elofsson Protein engineering 10 (6), 673-676, 1997 | 1372 | 1997 |