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Andrea Argentini
Andrea Argentini
Department of Medical Protein Research, VIB, Ghent, Belgium.
Verified email at ugent.be
Title
Cited by
Cited by
Year
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
612016
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
542015
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
352020
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
142020
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
132018
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
122018
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
102015
Ranking aggregation based on belief function theory
A Argentini
University of Trento, 2012
92012
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
82009
Ranking aggregation based on belief function
A Argentini, E Blanzieri
International Conference on Information Processing and Management of …, 2012
72012
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
42019
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
32018
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
32015
Unsupervised Learning of True Ranking Estimators using the Belief Function Framework
A Argentini, E Blanzieri
University of Trento, 2011
12011
Neighborhood Counting Measure Metric and Minimum Risk Metric: An Empirical Comparison
A Argentini, E Blanzieri
University of Trento, 2008
12008
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
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
2019
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
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