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Machine learning applications in proteomics research: how the past can boost the future

(2014) PROTEOMICS. 14(4-5). p.353-366
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Bioinformatics: from nucleotids to networks (N2N)
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.
Keywords
Machine learning, Bioinformatics, Pattern recognition, Shotgun proteomics, Standardization, TANDEM MASS-SPECTROMETRY, RETENTION TIME PREDICTION, PROTEIN INFERENCE PROBLEM, PHASE LIQUID-CHROMATOGRAPHY, SUPPORT VECTOR MACHINES, PEPTIDE MS/MS SPECTRA, SHOTGUN PROTEOMICS, DATABASE SEARCH, QUANTITATIVE PROTEOMICS, STATISTICAL-METHODS

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Citation

Please use this url to cite or link to this publication:

Chicago
Kelchtermans, Pieter, Wout Bittremieux, Kurt De Grave, Sven Degroeve, Jan Ramon, Kris Laukens, Dirk Valkenborg, Harald Barsnes, and Lennart Martens. 2014. “Machine Learning Applications in Proteomics Research: How the Past Can Boost the Future.” Proteomics 14 (4-5): 353–366.
APA
Kelchtermans, P., Bittremieux, W., De Grave, K., Degroeve, S., Ramon, J., Laukens, K., Valkenborg, D., et al. (2014). Machine learning applications in proteomics research: how the past can boost the future. PROTEOMICS, 14(4-5), 353–366.
Vancouver
1.
Kelchtermans P, Bittremieux W, De Grave K, Degroeve S, Ramon J, Laukens K, et al. Machine learning applications in proteomics research: how the past can boost the future. PROTEOMICS. 2014;14(4-5):353–66.
MLA
Kelchtermans, Pieter, Wout Bittremieux, Kurt De Grave, et al. “Machine Learning Applications in Proteomics Research: How the Past Can Boost the Future.” PROTEOMICS 14.4-5 (2014): 353–366. Print.
@article{4259628,
  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.},
  author       = {Kelchtermans, Pieter and Bittremieux, Wout and De Grave, Kurt and Degroeve, Sven and Ramon, Jan and Laukens, Kris and Valkenborg, Dirk and Barsnes, Harald and Martens, Lennart},
  issn         = {1615-9853},
  journal      = {PROTEOMICS},
  keyword      = {Machine learning,Bioinformatics,Pattern recognition,Shotgun proteomics,Standardization,TANDEM MASS-SPECTROMETRY,RETENTION TIME PREDICTION,PROTEIN INFERENCE PROBLEM,PHASE LIQUID-CHROMATOGRAPHY,SUPPORT VECTOR MACHINES,PEPTIDE MS/MS SPECTRA,SHOTGUN PROTEOMICS,DATABASE SEARCH,QUANTITATIVE PROTEOMICS,STATISTICAL-METHODS},
  language     = {eng},
  number       = {4-5},
  pages        = {353--366},
  title        = {Machine learning applications in proteomics research: how the past can boost the future},
  url          = {http://dx.doi.org/10.1002/pmic.201300289},
  volume       = {14},
  year         = {2014},
}

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