当前位置： SCI文献检索 > BMC GENOMICS期刊下所有文献 > Comparison of feature selection and classification for MALDI-MS data.

Comparison of feature selection and classification for MALDI-MS data.

Abstract：

INTRODUCTION: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. RESULTS: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. CONCLUSION: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.

journal_name

BMC Genomics

journal_title

BMC genomics

authors

Liu Q,Sung AH,Qiao M,Chen Z,Yang JY,Yang MQ,Huang X,Deng Y

doi

10.1186/1471-2164-10-S1-S3

subject

Has Abstract

pub_date

2009-07-07 00:00:00

pages

issn

1471-2164

pii

1471-2164-10-S1-S3

journal_volume

10 Suppl 1

pub_type

杂志文章

在线工具

Comparison of feature selection and classification for MALDI-MS data.