This article is part of the supplement: The 2008 International Conference on Bioinformatics & Computational Biology (BIOCOMP'08)
Comparison of feature selection and classification for MALDI-MS data
1 Department of Computer Science, New Mexico Tech, Socorro, NM 87801 USA
2 Institute for Complex Additive Systems Analysis, New Mexico Tech, Socorro, NM 87801, USA
3 Biostatistics Epidemiology Research Design Core, Center for Clinical and Translational Sciences, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
4 Harvard University P. O. Box 400888, Cambridge, MA 02140-0888, USA
5 National Human Genome Research Institute, National Institutes of Health (NIH), U.S. Department of Health and Human Services, Bethesda, MD 20852, USA
6 Conjugate and Medicinal Chemistry Laboratory, Division of Nuclear Medicine and Molecular Imaging and Center for Advanced Medical Imaging, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
7 SpecPro, Vicksburg, MS 39180, USA
8 Department of Biology Science, The University of Southern Mississippi, 118 College Dr., Hattiesburg, MS 39406, USA
BMC Genomics 2009, 10(Suppl 1):S3 doi:10.1186/1471-2164-10-S1-S3Published: 7 July 2009
In the classification of Mass Spectrometry (MS) proteomics data, peak detection, feature selection, and learning classifiers are critical to classification accuracy. To better understand which methods are more accurate when classifying data, some publicly available peak detection algorithms for Matrix assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS) data were recently compared; however, the issue of different feature selection methods and different classification models as they relate to classification performance has not been addressed. With the application of intelligent computing, much progress has been made in the development of feature selection methods and learning classifiers for the analysis of high-throughput biological data. The main objective of this paper is to compare the methods of feature selection and different learning classifiers when applied to MALDI-MS data and to provide a subsequent reference for the analysis of MS proteomics data.
We compared a well-known method of feature selection, Support Vector Machine Recursive Feature Elimination (SVMRFE), and a recently developed method, Gradient based Leave-one-out Gene Selection (GLGS) that effectively performs microarray data analysis. We also compared several learning classifiers including K-Nearest Neighbor Classifier (KNNC), Naïve Bayes Classifier (NBC), Nearest Mean Scaled Classifier (NMSC), uncorrelated normal based quadratic Bayes Classifier recorded as UDC, Support Vector Machines, and a distance metric learning for Large Margin Nearest Neighbor classifier (LMNN) based on Mahanalobis distance. To compare, we conducted a comprehensive experimental study using three types of MALDI-MS data.
Regarding feature selection, SVMRFE outperformed GLGS in classification. As for the learning classifiers, when classification models derived from the best training were compared, SVMs performed the best with respect to the expected testing accuracy. However, the distance metric learning LMNN outperformed SVMs and other classifiers on evaluating the best testing. In such cases, the optimum classification model based on LMNN is worth investigating for future study.