Reference : Proteomic mass spectra classification using decision tree based ensemble methods.
Scientific journals : Article
Human health sciences : Pharmacy, pharmacology & toxicology
http://hdl.handle.net/2268/18198
Proteomic mass spectra classification using decision tree based ensemble methods.
English
Geurts, Pierre mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Fillet, Marianne mailto [Université de Liège - ULg > Département de pharmacie > Analyse des médicaments >]
De Seny, Dominique mailto [Centre Hospitalier Universitaire de Liège - CHU > > Rhumatologie >]
Meuwis, Marie-Alice mailto [Université de Liège - ULg > > GIGA-Management : Plate-forme protéomique >]
Malaise, Michel mailto [Centre Hospitalier Universitaire de Liège - CHU > > Rhumatologie >]
Merville, Marie-Paule [Université de Liège - ULg > Département de pharmacie > Chimie médicale >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
2005
Bioinformatics
Oxford University Press - Journals Department
21
14
3138-45
Yes (verified by ORBi)
International
1367-4803
1460-2059
Oxford
United Kingdom
[en] Algorithms ; Amino Acid Sequence ; Artificial Intelligence ; Databases, Protein ; Decision Trees ; Mass Spectrometry/methods ; Molecular Sequence Data ; Pattern Recognition, Automated/methods ; Peptide Mapping/methods ; Proteome/analysis/chemistry ; Sequence Alignment/methods ; Sequence Analysis, Protein/methods
[en] 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.
FNRS
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/18198
also: http://hdl.handle.net/2268/11789
10.1093/bioinformatics/bti494

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