Machine Learning
Knowledge acquisition and development of accurate rules for predicting protein stability changes
Computational Biology and Chemistry
Induction of multiple criteria optimal classification rules for biological and medical data
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Random Forest for Gene Expression Based Cancer Classification: Overlooked Issues
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
A Clustering Based Hybrid System for Mass Spectrometry Data Analysis
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Classification of peptide mass fingerprint data by novel no-regret boosting method
Computers in Biology and Medicine
Is bagging effective in the classification of small-sample genomic and proteomic data?
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Mixture classification model based on clinical markers for breast cancer prognosis
Artificial Intelligence in Medicine
Decision forest for classification of gene expression data
Computers in Biology and Medicine
Small-sample error estimation for bagged classification rules
EURASIP Journal on Advances in Signal Processing - Special issue on genomic signal processing
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Fast Kernel Discriminant Analysis for Classification of Liver Cancer Mass Spectra
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data
Information Sciences: an International Journal
A note on hyper ellipse method for classifying biological and medical data
Computers in Biology and Medicine
Effective peak alignment for mass spectrometry data analysis using two-phase clustering approach
International Journal of Data Mining and Bioinformatics
Hi-index | 3.84 |
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