title: Machine learning applications in proteomics research: how the past can boost the future.
abstract: Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution to a particularly intractable problem, given that enough data are available to train and subsequently evaluate an algorithm on. Since MS-based proteomics has no shortage of complex problems, and since publicly available data are becoming available in ever growing amounts, machine learning is fast becoming a very popular tool in the field. We here therefore present an overview of the different applications of machine learning in proteomics that together cover nearly the entire wet- and dry-lab workflow, and that address key bottlenecks in experiment planning and design, as well as in data processing and analysis.
authors: Department of Medical Protein Research, VIB, Ghent, Belgium; Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium; Flemish Institute for Technological Research (VITO), Boeretang, Mol, Belgium.
authors: Pieter
authors: P
authors: Kelchtermans
authors: Wout
authors: W
authors: Bittremieux
authors: Kurt
authors: K
authors: De Grave
authors: Sven
authors: S
authors: Degroeve
authors: Jan
authors: J
authors: Ramon
authors: Kris
authors: K
authors: Laukens
authors: Dirk
authors: D
authors: Valkenborg
authors: Harald
authors: H
authors: Barsnes
authors: Lennart
authors: L
authors: Martens
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology; D040901:Proteomics / Q000379:methods*; D012015:Reference Standards; D012107:Research Design
keywords: Bioinformatics; Machine learning; Pattern recognition; Shotgun proteomics; Standardization