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学科主题: 分析化学
题名: Feature Grouping Technique Based on Biclustering for the Analysis of LC-MS Metabolomic Data
作者: Lin XH(林晓惠) ;  Ruan Q(阮强) ;  Zhou LN(周丽娜) ;  Yin PY(尹沛源) ;  Xu GW(许国旺)
会议文集: Proceeding of HPLC 2011
会议名称: 37th International Symposium on High Performance Liquid Phase Separations and Related Techniques
会议日期: 2011-10-8
出版日期: 2011
会议地点: 大连
通讯作者: 许国旺
出版者: 待补充
出版地: 待补充
合作性质: 墙报
部门归属: 1808
主办者: 中国化学会色谱专业委员会
摘要: Metabolomics has shown a promising application in many fields such as disease diagnosis, drug development. Liquid chromatography-mass spectrometry (LC-MS) is one of its main analysis techniques. HPLC-MS metabolomic data is usually of high dimension. Among the large features, some are related to each other and contain the similar information about the problem. Grouping the features correctly is very meaningful for getting a comprehension of the problem studied and building a more efficient classification model. In this work, a LC-MS dataset which contains serum specimens from 30 normal samples, 30 hepatitis patients (H), 30 cirrhosis patients (C) and 30 liver cancers patients (T) was got. To distinguish the different kinds of the liver disease, we proposed an ensemble classification method based on the feature grouping by the biclustering [1] technique (EC-BicFG). For each base classifier, the feature subspace is generated according to the group ranking. Naive Bayes (NB) and 5-nearest-neighbor (5NN) are adopted as the base classifiers, respectively. In addition to discriminating between controls and patients, we also conducted the experiments to distinguish among three liver diseases, and between each two kinds of the liver diseases. The corresponding accuracy rates are listed in Table 1. It shows that our method out performs EGSG [2] which is also an ensemble classification algorithm based on feature grouping. Table 1 The LOOCV classification accuracy rates NB (%) 5NN (%) EGSG EC-BicFG EGSG EC-BicFG H vs. C 80.50(2.61) 87.67(1.61) 71.99(5.49) 84.50(1.77) H vs. T 76.50(5.41) 86.33(2.58) 60.00(5.21) 77.67(1.41) C vs. T 76.50(4.93) 81.00(2.63) 54.00(3.78) 83.83(1.24) H vs. C vs. T 62.33(5.84) 73.11(0.47) 54.33(3.41) 72.11(1.77) Control vs. Model 95.42(1.85) 96.83(1.61) 89.75(2.69) 97.42(1.07) REFERENCE [1] Yizong Cheng, George M. Church. Biclustering of expression data. In Proceedings of 8th International Conference on Intelligent System for Molecular Biology (ISMB) (2000) 93–103. [2] Huawen Liu, Lei Liu, Huijie Zhang. Ensemble gene selection by grouping for microarray data classification. Journal of Biomedical Informatics 43 (2010) 81–87.
英文摘要: Metabolomics has shown a promising application in many fields such as disease diagnosis, drug development. Liquid chromatography-mass spectrometry (LC-MS) is one of its main analysis techniques. HPLC-MS metabolomic data is usually of high dimension. Among the large features, some are related to each other and contain the similar information about the problem. Grouping the features correctly is very meaningful for getting a comprehension of the problem studied and building a more efficient classification model. In this work, a LC-MS dataset which contains serum specimens from 30 normal samples, 30 hepatitis patients (H), 30 cirrhosis patients (C) and 30 liver cancers patients (T) was got. To distinguish the different kinds of the liver disease, we proposed an ensemble classification method based on the feature grouping by the biclustering [1] technique (EC-BicFG). For each base classifier, the feature subspace is generated according to the group ranking. Naive Bayes (NB) and 5-nearest-neighbor (5NN) are adopted as the base classifiers, respectively. In addition to discriminating between controls and patients, we also conducted the experiments to distinguish among three liver diseases, and between each two kinds of the liver diseases. The corresponding accuracy rates are listed in Table 1. It shows that our method out performs EGSG [2] which is also an ensemble classification algorithm based on feature grouping. Table 1 The LOOCV classification accuracy rates NB (%) 5NN (%) EGSG EC-BicFG EGSG EC-BicFG H vs. C 80.50(2.61) 87.67(1.61) 71.99(5.49) 84.50(1.77) H vs. T 76.50(5.41) 86.33(2.58) 60.00(5.21) 77.67(1.41) C vs. T 76.50(4.93) 81.00(2.63) 54.00(3.78) 83.83(1.24) H vs. C vs. T 62.33(5.84) 73.11(0.47) 54.33(3.41) 72.11(1.77) Control vs. Model 95.42(1.85) 96.83(1.61) 89.75(2.69) 97.42(1.07) REFERENCE [1] Yizong Cheng, George M. Church. Biclustering of expression data. In Proceedings of 8th International Conference on Intelligent System for Molecular Biology (ISMB) (2000) 93–103. [2] Huawen Liu, Lei Liu, Huijie Zhang. Ensemble gene selection by grouping for microarray data classification. Journal of Biomedical Informatics 43 (2010) 81–87.
内容类型: 会议论文
URI标识: http://cas-ir.dicp.ac.cn/handle/321008/116075
Appears in Collections:中国科学院大连化学物理研究所_会议论文

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Recommended Citation:
Lin XH,Ruan Q,Zhou LN,et al. Feature Grouping Technique Based on Biclustering for the Analysis of LC-MS Metabolomic Data[C]. 见:37th International Symposium on High Performance Liquid Phase Separations and Related Techniques. 大连. 2011-10-8.
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