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基于DNA微阵列基因表达数据的分类方法研究 被引量:1

Study of Classification Methods Based on DNA Gene Expression Microarray Data
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摘要 介绍了目前几种基于DNA微阵列基因表达数据的分类方法。分别阐述了递归分割法、构建森林法以及信息融合方法的算法思想,对每种方法进行了深入描述,并对它们进行了分析和比较。最后对基于基因表达微阵列数据的分类技术进行了展望。 Several classification methods based on DNA gene expression microarray data are introduced in this paper.The methods of recursive partitioning,construction forests and information fusion and their steps are respectively expati-ated and described in detail.This paper also analyses and compares these methods.At last the develop directions of clas-sification technique based on gene expression microarray data are indicated.
出处 《计算机工程与应用》 CSCD 北大核心 2005年第6期171-174,共4页 Computer Engineering and Applications
基金 国家自然科学基金项目(编号:60374070) 广东省自然科学基金项目(编号:031903)
关键词 微阵列数据 基因表达 分类 microarray data,gene expression,classification
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参考文献9

  • 1Heping Zhang,Chang-Yung Yu,Burton Singer et al. Recursive parti tioning for tumor classification with gene expression microarray data [C].In:Proceedings of the National Academy of Sciences of the United States of America, http://www.pnas.org/cgi/doi/10.1073/pnas.111153698,2001; 98 (12): 6730~6735.
  • 2Heping Zhang,Chang-Yung Yu,Burton Singer. Cell and tumor classification using gene expression data :Construction of forests[J].Proceed ings of the National Academy of Sciences of the United States ofAmerica. http: //www. pnas. org/cgi / doi / 10.1073/pnas. 0230559100,2003; 100(7) :4168~4172.
  • 3Benjamin I P Rubinstein,Jon McAuliffe,Simon Cawley et al. Machine Learning in Low-level Microarray Analysis[J].ACM SIGKDD Explorations NewsLetter, 2003; 5 (2): 130~ 139.
  • 4Sung-Bae cho,Hong-hee Won. Machine Learning in DNA Microarray Analysis for Cancer Classification[C].In :Proceedings of the First Asia-Pacific bioinformatics Conference on Bioinformatie ,Conferences in Research and Practice in Information Technology,Adelaide,Australia, 2003:19.
  • 5Jyotsna Kasturi,Raj Acharya. Clustering of Diverse Genomic Data using Information Fusion[C].In:ACM Symposium on Applied Computing,2004:116~120.
  • 6Wentian Li,Ivo Grosse. Gene Selection Criterion for Discriminant Microarray Data Analysis Based on Extreme Value Distributions[C]. In: RECOMB03 ′, Berlin, Germany, 2003-04:217~223.
  • 7Iiya Shmulevich,Jaakko Astola,David Cogdell et al. Data extraction from composite oligonucleotide microarrays[J].Nucleie Acids Research, 2003; 31 (7e36).
  • 8Jason C Mills,Jeffrey I Gordon.A new approach for filtering noise from high-density oligonucleotide microarray datasets[J].Nueleic AcidsResearch,2001 ;29(15 e72).
  • 9Blaise Hanczar,Melanie Courtine,Arriel Benis et al. Improving classification of microarray data using prototype-based feature selection [J].ACM SIGKDD Explorations Newsletter, 2003; 5 (2): 23~30.

同被引文献14

  • 1朱婵,许龙飞.聚类算法在基因表达数据分析中的应用[J].华侨大学学报(自然科学版),2005,26(1):7-10. 被引量:4
  • 2玄光男 程润伟.遗传算法与工程优化[M].北京:清华大学出版社,2004..
  • 3Jia wei Han.Micheline Kamber.范明,孟小峰译..数据挖掘:概念与技术[M]..北京:机械工业出版社,,2002..223~257..
  • 4Annaka Kalton,Kiri Wagstaff,Jungsoon Yoo.Generalized Clustering, Supervised Learning,and Data Assignment[J].KDD ,2001;(1):299-304
  • 5J C Bezdek.Pattem Recognition with Fuzzy Objective Function Algorithms[M].New York:Plenum Press,1987
  • 6Yager R R,Filev D P.Approximating Clustering via the Mountain Method[J].IEEE Trans SMC, 1994;24(8):1279-1284
  • 7Reginald E Hammah ,John H Curran,Validity Measures for the Fuzzy Cluster Analysis of Orientations[J].IEEE Transaction on Pattern Analysis and Machine Intellignce, 2000: 22(12)
  • 8X L Xie,G Beni.A Validity Measure for Fuzzy Clustering[J].IEEE Trans PAMI, 1991; 13(8):841-847
  • 9K Krishna,M Narasimha Murty,Genetie K-Means Algorithm[J].IEEE Transactions on Systems, Man, and Cybernet cs Patr.B : Cybernetics,1999;29(3)
  • 10S T Spellman.Comprehensive Identification of Cell Cycle-regulated Genes of the Yeast Saecharomyces Cerevisiae by Mieroarray Hybridization[J].Molecular Biology of the Cell, 1998-12;9:3273-3297

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