Journal Article

Proteomic mass spectra classification using decision tree based ensemble methods

Pierre Geurts, Marianne Fillet, Dominique de Seny, Marie-Alice Meuwis, Michel Malaise, Marie-Paule Merville and Louis Wehenkel

in Bioinformatics

Volume 21, issue 14, pages 3138-3145
Published in print July 2005 | ISSN: 1367-4803
Published online May 2005 | e-ISSN: 1460-2059 | DOI: https://dx.doi.org/10.1093/bioinformatics/bti494
Proteomic mass spectra classification using decision tree based ensemble methods

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Motivation: Modern mass spectrometry allows the determination of proteomic fingerprints of body fluids like serum, saliva or urine. These measurements can be used in many medical applications in order to diagnose the current state or predict the evolution of a disease. Recent developments in machine learning allow one to exploit such datasets, characterized by small numbers of very high-dimensional samples.

Results: We propose a systematic approach based on decision tree ensemble methods, which is used to automatically determine proteomic biomarkers and predictive models. The approach is validated on two datasets of surface-enhanced laser desorption/ionization time of flight measurements, for the diagnosis of rheumatoid arthritis and inflammatory bowel diseases. The results suggest that the methodology can handle a broad class of similar problems.

Supplementary information: Additional tables, appendicies and datasets may be found at http://www.montefiore.ulg.ac.be/~geurts/Papers/Proteomic-suppl.html

Contact: p.geurts@ulg.ac.be

Journal Article.  6008 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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