Designing biomedical proteomics experiments: state-of-the-art and future perspectives

Evelyne Maes (1, 2) , Pieter Kelchtermans (3, 3) , Wout Bittremieux (4) , Kurt de Grave (5) , Sven Degroeve (3, 3) , Jef Hooyberghs (2) , Inge Mertens (1, 2) , Geert Baggerman (1, 2) , Jan Ramon (6, 5) , Kris Laukens (4) , Lennart Martens (3, 3) , Dirk Valkenborg (7, 1, 2)

1 UA - University of Antwerp
2 VITO - Vlaamse Instelling voor Technologisch Onderzoek [Mol]
3 UGENT - Universiteit Gent = Ghent University
4 Department of Mathematics and Computer Science
5 Department of Computer Science - K.U.Leuven
6 MAGNET - Machine Learning in Information Networks
7 UHasselt - Hasselt University

With the current expanded technical capabilities to perform mass spectrometry-based biomedi-cal proteomics experiments, an improved focus on the design of experiments is crucial. As it is clear that ignoring the importance of a good design leads to an unprecedented rate of false discoveries which would poison our results, more and more tools are developed to help researchers designing proteomic experiments. In this review, we apply statistical thinking to go through the entire proteomics workflow for biomarker discovery and validation and relate the considerations that should be made at the level of hypothesis building, technology selection, experimental design and the optimization of the experimental parameters.

Domaines: Intelligence artificielle [cs.AI]

Evelyne Maes, Pieter Kelchtermans, Wout Bittremieux, Kurt de Grave, Sven Degroeve, et al.. Designing biomedical proteomics experiments: state-of-the-art and future perspectives. Expert Review of Proteomics, 2016, ⟨10.1586/14789450.2016.1172967⟩. ⟨hal-01431414⟩