Andrea Argentini
Andrea Argentini
Department of Medical Protein Research, VIB, Ghent, Belgium.
Verified email at ugent.be
TitleCited byYear
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
252015
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, 2016
212016
Ranking aggregation based on belief function theory
A Argentini
University of Trento, 2012
82012
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
72018
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
72009
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
62015
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
22018
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
22015
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
12018
Ranking Aggregation Based on Belief Function
A Argentini, E Blanzieri
International Conference on Information Processing and Management of …, 2012
12012
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
A simple approach for accurate peptide quantification in MS-based proteomics
TMM Maia, A Staes, K Plasman, J Pauwels, K Boucher, A Argentini, ...
bioRxiv, 703397, 2019
2019
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
2019
Evaluation of moFF and FlashLFQ for label free peptide quantification in proteomic workflows within Galaxy-P Framework.
S Mehta, C Easterly, JE Johnson, B Grüning, A Argentini, RJ Millikin, ...
F1000Research 7, 2018
2018
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), 2015
2015
Unsupervised Learning of True Ranking Estimators from Small-size Permutation Samples
A Argentini, E Blanzieri
2011
A Hierarchical Bayesian Network for the Optimization of SRM Assays
J Renaux, A Argentini, J Ramon
29th Benelux Conference on Artificial Intelligence November 8–9, 2017 …, 0
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Articles 1–18