全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于传输互表达的基因表达数据聚类分析

, PP. 894-899

Keywords: 基因表达数据,聚类,表达相似,功能相似,传输互表达

Full-Text   Cite this paper   Add to My Lib

Abstract:

针对基因表达数据基于表达相似的聚类分析并不能完全揭示基因之间的功能相似问题,结合基因的传输互表达关系,提出基于传输互表达的聚类分析方法。首先用基因的表达相关来构建基因相关图,然后通过最短路分析来获得基因之间传输互表达关系并作为基因的相似测度,再用k-均值聚类算法进行聚类分析。对Yeast基因表达数据进行聚类实验,并与基于表达相似的聚类结果对比。实验结果表明,基于传输互表达的聚类方法能获得更好的聚类性能和较高的聚类正确率,验证基于传输互表达的基因聚类更能揭示基因相似的本质。

References

[1]  Karakach T K,Flight R M,Douglas S E,et al.An Introduction to DNA Microarrays for Gene Expression Analysis.Chemometrics and Intelligent Laboratory Systems,2010,104(1): 28-52
[2]  Katagiri F,Glazebrook J.Overview of mRNA Expression Profiling Using DNA Microarrays.[EB/OL].[2011-09-01].http://www.current protocals.com/wileyCDA/CPUnit/refid-mb2204.html
[3]  Nguyen D V,Arpat A B,Wang N,et al.DNA Microarray Experiments: Biological and Technological Aspects.Biometrics,2002,58(4): 701-717
[4]  Verducci J S,Melfi V F,Lin S,et al.Microarray Analysis of Gene Expression: Considerations in Data Mining and Statistical Treatment.Physiological Genomics,2006,25(3): 355-363
[5]  Kerr G,Ruskin H J,Crane M,et al.Techniques for Clustering Gene Expression Data.Computers in Biology and Medicine,2008,38(3): 283-293
[6]  Jung K,Grade M,Gaedcke J,et al.A New Sensitivity-Preferred Strategy to Build Prediction Rules for Therapy Response of Cancer Patients Using Gene Expression Data.Computer Methods and Programs in Biomedicine,2010,100(2): 132-139
[7]  Xu R,Damelin S,Nadler B,et al.Clustering of High-Dimensional Gene Expression Data with Feature Filtering Methods and Diffusion Maps.Artificial Intelligence in Medicine,2010,48(2/3): 91-98
[8]  An J,Chen Y P.Finding Rule Groups to Classify High Dimensional Gene Expression Datasets.Computational Biology and Chemistry,2009,33(1): 108-113
[9]  Lin K S,Chien C F.Cluster Analysis of Genome-Wide Expression Data for Feature Extraction.Expert Systems with Applications,2009,36(2): 3327-3335
[10]  Song J,Nicolae D L.A Sequential Clustering Algorithm with Applications to Gene Expression Data.Journal of the Korean Statistical Society,2009,38(2): 175-184
[11]  Lu Xinguo,Lin Yaping,Wang Haijun,et al.A Relative Space Based Cancer Classification with Gene Expression Profiles.Acta Electronica Sinica,2008,36 (4): 614-619 (in Chinese) (卢新国,林亚平,王海军,等.基于微阵列基因表达谱的一种关联空间的癌症分类算法.电子学报,2008,36(4): 614-619)
[12]  Li Yinxin,Liu Quanjin,Ruan Xiaogang.A Method for Extracting Knowledge from Tumor Gene Expression Data.Acta Electronica Sinica,2004,32(9): 1479-1482 (in Chinese) (李颖新,刘全金,阮晓钢.一种肿瘤基因表达数据的知识提取方法.电子学报,2004,32(9): 1479-1482)
[13]  Karypis G,Han E H,Kumar V.CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling .Journal of Computer,1999,32(8): 68-75
[14]  Eisen M B,Spellman P T,Brown P O,et al.Cluster Analysis and Display of Genome-Wide Expression Patterns.Proc of the National Academy of Science of the USA,1998,95(25): 14863-14868
[15]  Herwig R,Poustka A J,Müller C,et al.Large-Scale Clustering of cDNA-Fingerprinting Data.Proc of the National Academy of Science,1999,9(11): 1093-1105
[16]  Kohonen T.The Self-Organizing Map.Proc of IEEE,1990,78(9): 1464-1480
[17]  Gong Gaiyun,Mao Yongcai,Gao Xinbo,et al.Fuzzy C-mean Clustering Method for Analyzing Microarray Gene Expression Data.Journal of Xidian University,2004,31(2): 291-295 (in Chinese) (宫改云,毛用才,高新波,等.模糊C-均值聚类的微阵列基因表达数据分析.西安电子科技大学学报,2004,31(2): 291-295)
[18]  Zhou X,Kao M C,Hung W W.Transitive Functional Annotation by Shortest-Path Analysis of Gene Expression Data.Proc of the National Academy of Sciences of the USA,2002,99(20): 12783-12788
[19]  Qu Y,Xu S Z.Supervised Cluster Analysis for Microarray Data Based on Multivariate Gaussian Mixture.Bioinformatics,2004,20(12): 1905-1913

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133