| moFF: a robust and automated approach to extract peptide ion intensities A Argentini, LJE Goeminne, K Verheggen, N Hulstaert, A Staes, ... Nature methods 13 (12), 964-966, 2016 | 64 | 2016 |
| Summarization vs peptide-based models in label-free quantitative proteomics: performance, pitfalls, and data analysis guidelines LJE Goeminne, A Argentini, L Martens, L Clement Journal of proteome research 14 (6), 2457-2465, 2015 | 52 | 2015 |
| Simple peptide quantification approach for MS-based proteomics quality control TM Maia, A Staes, K Plasman, J Pauwels, K Boucher, A Argentini, ... ACS omega 5 (12), 6754-6762, 2020 | 37 | 2020 |
| Precursor intensity-based label-free quantification software tools for proteomic and multi-omic analysis within the galaxy platform S Mehta, CW Easterly, R Sajulga, RJ Millikin, A Argentini, I Eguinoa, ... Proteomes 8 (3), 15, 2020 | 15 | 2020 |
| Update on the moFF algorithm for label-free quantitative proteomics A Argentini, A Staes, B Grüning, S Mehta, C Easterly, TJ Griffin, P Jagtap, ... Journal of proteome research 18 (2), 728-731, 2018 | 13 | 2018 |
| NES2RA: Network expansion by stratified variable subsetting and ranking aggregation F Asnicar, L Masera, E Coller, C Gallo, N Sella, T Tolio, P Morettin, ... The International Journal of High Performance Computing Applications 32 (3 …, 2018 | 12 | 2018 |
| Discovering candidates for gene network expansion by distributed volunteer computing F Asnicar, L Erculiani, F Galante, C Gallo, L Masera, P Morettin, N Sella, ... 2015 IEEE Trustcom/BigDataSE/ISPA 3, 248-253, 2015 | 10 | 2015 |
| Ranking aggregation based on belief function theory A Argentini University of Trento, 2012 | 9 | 2012 |
| About neighborhood counting measure metric and minimum risk metric A Argentini, E Blanzieri IEEE transactions on pattern analysis and machine intelligence 32 (4), 763-765, 2009 | 8 | 2009 |
| Ranking aggregation based on belief function A Argentini, E Blanzieri International Conference on Information Processing and Management of …, 2012 | 6 | 2012 |
| A well-ordered nanoflow LC–MS/MS approach for proteome profiling using 200 cm long micro pillar array columns JO De Beeck, J Pauwels, N Van Landuyt, P Jacobs, W De Malsche, ... BioRxiv, 472134, 2019 | 4 | 2019 |
| Digging deeper into the human proteome: A novel nanoflow LCMS setup using micro pillar array columns (μPAC™) JO De Beeck, J Pauwels, A Staes, N Van Landuyt, D Van Haver, ... bioRxiv, 472134, 2018 | 3 | 2018 |
| Open-source, platform-independent library and online scripting environment for accessing thermo scientific RAW files P Kelchtermans, ASC Silva, A Argentini, A Staes, J Vandenbussche, ... Journal of Proteome Research 14 (11), 4940-4943, 2015 | 3 | 2015 |
| A simple approach for accurate peptide quantification in MS-based proteomics TM Maia, A Staes, K Plasman, J Pauwels, K Boucher, A Argentini, ... bioRxiv, 703397, 2019 | 1 | 2019 |
| Unsupervised Learning of True Ranking Estimators using the Belief Function Framework A Argentini, E Blanzieri University of Trento, 2011 | 1 | 2011 |
| Neighborhood Counting Measure Metric and Minimum Risk Metric: An Empirical Comparison A Argentini, E Blanzieri University of Trento, 2008 | 1 | 2008 |
| Development and Optimization of a Subtraction-Normalized Immunocyte Profiling Signature for Prostate Cancer Active Surveillance Risk Stratification L Van Neste, R Henao, KJ Wojno, J Signes, J DeHart, A Busta, E Marriott, ... The Journal of urology 211 (3), 415-425, 2024 | | 2024 |
| Using moFF to Extract Peptide Ion Intensities from LC-MS experiments L Martens, A Argentini, LJE Goeminne, K Verheggen, N Hulstaert, A Staes, ... | | 2016 |
| Discovering candidates for gene network expansion by variable subsetting and ranking aggregation L Erculiani, F Galante, C Gallo, F Asnicar, L Masera, P Morettin, N Sella, ... Network Biology Community-ISMB meeting (NetBio _SIG_2015), Dublin, Ireland …, 2015 | | 2015 |
| International journals Y Wang, H Sun, W Du, E Blanzieri, G Viero, Y Liang, CM Livi, Z Cao, ... BMC Bioinformatics 15 (123), 2014 | | 2014 |
