Proteomic mass spectra classification using decision tree based ensemble methods

Abstract

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.

Keywords

Decision treeEnsemble learningComputer scienceTree (set theory)Mass spectrumDecision tree learningArtificial intelligenceMachine learningPattern recognition (psychology)Data miningMass spectrometryMathematicsChemistryChromatographyCombinatorics

Affiliated Institutions

University of Liège BE

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Publication Info

Year: 2005
Type: article
Volume: 21
Issue: 14
Pages: 3138-3145
Citations: 131
Access: Closed

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APA Style

                            
                                
                                    Pierre Geurts, 
                                
                                    Marianne Fillet, 
                                
                                    Dominique de Seny
                                
                                et al.
                            
                            (2005). 
                            Proteomic mass spectra classification using decision tree based ensemble methods. 
                            Computer applications in the biosciences
                            , 21
                            (14)
                            , 3138-3145.
                            https://doi.org/10.1093/bioinformatics/bti494
                        

Identifiers

DOI: 10.1093/bioinformatics/bti494