Automatic Classification of NMR Spectra by Ensembles of Local Experts

A new approach for the automatic detection of drug-induced organ toxicities based on Nuclear Magnetic Resonance Spectroscopy data from biofluids is presented in this paper. Spectral data from biofluids contain information on the concentration of various substances, but the combination of only a small subset of these cues is putatively useful for classification of new samples. We propose to divide the spectra into several short regions and train classifiers on them, using only a limited amount of information for class discrimination. These local experts are combined in an ensemble classification system and the subset of experts for the final classification is optimized automatically. Thus, only local experts for relevant spectral regions are used for the final ensemble classification. The proposed approach has been evaluated on a real data-set from industrial pharmacology, showing an improvement in classification accuracy and indicating relevant spectral regions for classification.

[1]  Rasmus Bro,et al.  Automated alignment of chromatographic data , 2006 .

[2]  R. Barnes,et al.  Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra , 1989 .

[3]  E Holmes,et al.  Development of a model for classification of toxin‐induced lesions using 1H NMR spectroscopy of urine combined with pattern recognition , 1998, NMR in biomedicine.

[4]  James C. Bezdek,et al.  Decision templates for multiple classifier fusion: an experimental comparison , 2001, Pattern Recognit..

[5]  Henrik Antti,et al.  Contemporary issues in toxicology the role of metabonomics in toxicology and its evaluation by the COMET project. , 2003, Toxicology and applied pharmacology.

[6]  Kai Lienemann,et al.  NMR-based urine analysis in rats: prediction of proximal tubule kidney toxicity and phospholipidosis. , 2008, Journal of pharmacological and toxicological methods.

[7]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[8]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  B. Karlberg,et al.  New modes of data partitioning based on PARS peak alignment for improved multivariate biomarker/biopattern detection in 1H-NMR spectroscopic metabolic profiling of urine , 2006, Metabolomics.

[10]  Lefteri H. Tsoukalas,et al.  Neural network methodology for /sup 1/H NMR spectroscopy classification , 1999, Proceedings 1999 International Conference on Information Intelligence and Systems (Cat. No.PR00446).

[11]  Kai Lienemann,et al.  On the Application of SVM-Ensembles Based on Adapted Random Subspace Sampling for Automatic Classification of NMR Data , 2007, MCS.

[12]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[13]  T. Ebbels,et al.  NMR-based metabonomic toxicity classification: hierarchical cluster analysis and k-nearest-neighbour approaches , 2003 .

[14]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[15]  E Holmes,et al.  Automatic reduction of NMR spectroscopic data for statistical and pattern recognition classification of samples. , 1994, Journal of pharmaceutical and biomedical analysis.

[16]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[17]  S. Jacobsson,et al.  Multivariate analysis of NMR spectra for saponins from Quillaja saponaria Molina , 2001 .

[18]  David G. Stork,et al.  Pattern Classification , 1973 .

[19]  R. Freeman.  Magnetic Resonance in Chemistry and Medicine , 2003 .

[20]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Ralf J. O. Torgrip,et al.  Peak alignment using reduced set mapping , 2003 .

[22]  B. Matthews.  Comparison of the predicted and observed secondary structure of T4 phage lysozyme. , 1975, Biochimica et biophysica acta.