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Items: 5

1.

Update on the moFF Algorithm for Label-Free Quantitative Proteomics.

Argentini A, Staes A, GrĂ¼ning B, Mehta S, Easterly C, Griffin TJ, Jagtap P, Impens F, Martens L.

J Proteome Res. 2019 Feb 1;18(2):728-731. doi: 10.1021/acs.jproteome.8b00708. Epub 2018 Dec 14.

PMID:
30511867
2.

moFF: a robust and automated approach to extract peptide ion intensities.

Argentini A, Goeminne LJ, Verheggen K, Hulstaert N, Staes A, Clement L, Martens L.

Nat Methods. 2016 Nov 29;13(12):964-966. doi: 10.1038/nmeth.4075. No abstract available.

PMID:
27898063
3.

Open-Source, Platform-Independent Library and Online Scripting Environment for Accessing Thermo Scientific RAW Files.

Kelchtermans P, Silva AS, Argentini A, Staes A, Vandenbussche J, Laukens K, Valkenborg D, Martens L.

J Proteome Res. 2015 Nov 6;14(11):4940-3. doi: 10.1021/acs.jproteome.5b00778. Epub 2015 Oct 29.

PMID:
26477298
4.

Summarization vs Peptide-Based Models in Label-Free Quantitative Proteomics: Performance, Pitfalls, and Data Analysis Guidelines.

Goeminne LJ, Argentini A, Martens L, Clement L.

J Proteome Res. 2015 Jun 5;14(6):2457-65. doi: 10.1021/pr501223t. Epub 2015 May 7.

PMID:
25827922
5.

About neighborhood counting measure metric and minimum risk metric.

Argentini A, Blanzieri E.

IEEE Trans Pattern Anal Mach Intell. 2010 Apr;32(4):763-5; discussion 766-8. doi: 10.1109/TPAMI.2009.69.

PMID:
20224130

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