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建立同类提取法从基因芯片检测数据中挖掘大鼠肝细胞的肝再生关键基因 被引量:1

Establishing Same Kind Extraction Method to Find the Hepatocyte Key Genes in Rat Liver Regeneration from the Data of Gene Microarray
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摘要 为解析基因芯片检测数据的生物学意义,用Rat Genome 230 2.0芯片检测大鼠肝再生中肝细胞的基因表达丰度,用F检验大鼠2/3肝切除组(PH)与假手术组(SO)的基因表达差异性和获得大鼠肝细胞的肝再生相关基因,用同类提取法从过滤法计算的差异基因中筛选特征基因,根据特征基因的关联度、参与的生理活动和他人研究结果确认其中的关键基因.结果表明,Ccne1、Egf、Met等57个特征基因在大鼠肝再生中起关键作用. To highlight the biological significance of gene microarray data,Rat Genome 230 2.0 Arrays were used to detect expression abundance of the hepatocyte genes in rat liver regeneration,F-test to test the gene expression differences and to identify the liver regeneration associated-genes in rat two-thirds hepatectomy(PH)and sham operation(SO),same kind extraction method to select the hepatocyte feature genes in rat liver regeneration,which came from the differential genes evaluated by Filter method,then the liver regeneration key genes of rat hepatocytes were identified according to their connectivities,physiological activities and research results.The research shows that Ccne1,Egf,Met,etc 57 feature genes played key roles in the rat liver regeneration.
出处 《河南科学》 2013年第6期762-767,共6页 Henan Science
基金 国家973项目前期研究专项基金资助项目(2010CB534905)
关键词 大鼠肝再生 过滤法 同类提取法 差异基因 特征基因 关键基因 rat liver regeneration filter method same kind extraction method differential genes feature genes key genes
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参考文献7

  • 1Deutseh J M. Evolutionary algorithms for finding optimal gene sets in microarray prediction El]. Bioinform, 2003, 19 (1) :45-52.
  • 2Lazar C, Taminau J, Meganck S, et al. A survey on filter techniques for feature selection in gene expression microarray analysis [J] IEEE/ACM Trans Comput Biol Bioinform, 2012, 9 (4) .. 1106-1119.
  • 3Sell S. The hepatocyte: heterogeneity and plasticity of liver cells[J]. Int J Biochem Cell Biol, 2003,35 (3) : 267-271.
  • 4符庆瑛,高钰琪.大规模高通量方法在蛋白质相互作用研究中的应用[J].生物化学与生物物理进展,2008,35(3):246-254. 被引量:1
  • 5Li H,Zhou H, Wang D, et al. Versatile pathway-centric approach based on high-throughput sequencing to anticancer drug discovery [J]. Proc Natl Acad Sci USA, 2012, 109 (12) : 4609-4014.
  • 6邹晶,高磊,李晋,戴静珠,李霞.针对不同特征基因挖掘方法的特征基因功能一致性分析[J].中国生物医学工程学报,2010,29(2):212-219. 被引量:3
  • 7Wang J Z, Du Z, Payattakool R, et al. A new method to measure the semantic similarity of GO terms [J]. Bioinform, 2007, 23 (10): 1274-1281.

二级参考文献67

  • 1黄英,蔡雪飞,何茂锐,张君,黄爱龙.T7噬菌体展示技术筛选丙型肝炎病毒非结构蛋白3的相互作用蛋白[J].中华肝脏病杂志,2006,14(8):561-564. 被引量:1
  • 2Gilks CB,Vanderhyden BC,Zhu S,et al.Distinction between serous tumors of low malignant potential and serous carcinomas based on global mRNA expression profiling[J].Gynecologic Oncology,2005,96(3):684-694.
  • 3Richardson AL,Wang ZC,De Nicolo A,et al.X chromosomal abnormalities in basal-like human breast cancer[J].Cancer cell,2006,9(2):121-132.
  • 4Wang Dong,Lv Yingli,Li Xia,et al.Effects of replacing the unreliable cDNA microarray measurements on the disease classification based on gene expression profiles and functional modules[J],Bioinformatics,2006,22(23):2883-2889.
  • 5Murthy SK,Kasif S,Salzberg S.A system for induction of oblique decision trees[J].J Artif Intell Res,1994,2:1–32.
  • 6Su Yang,Murali TM,Pavlovic V,et al.Rankgene:a program to rank genes from expression data[EB/OL].http://genomics10.bu.edu/yangsu/rankgene/,2002-11-18/2009-9-17.
  • 7Su Yang,Murali TM,Pavlovic V,et al.Training support vector machines in 1D[EB/OL],http://genomics10.bu.edu/yangsu/rankgene/oned-svm.pdf,2002-9-8/2009-9-17.
  • 8Ashburner M,Ball CA,Blake JA,et al.Gene ontology:tool for the unification of biology gene ontology[J].Nature Genetics,2000,25 (1):25-29.
  • 9Rieck K,Laskov P,Sonnenburg S.Computation of similarity measures for sequential data using generalized suffix trees[J].The Journal of Machine Learning Research,2008,9:23-48.
  • 10Kim TY,Lee HJ,Hwang KS,et al.Methylation of RUNX3 in various types of human cancers and premalignant stages of gastric carcinoma[J].Laboratory Investigation,2004,84:479–484.

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同被引文献9

  • 1Saeys Y, Inza I, Larra~aga P. A review of feature selection techniques in bioinformatics [J]. Bioinformatics, 2007, 23 (19) : 7-17.
  • 2Kohavi R, John G H. The wrapper approach [C]//The International Series in Engineering and Computer Science. New York: Springer US, 1998,453: 33-50.
  • 3Larsson O, Wahlestedt C, Timmons J A. Considerations when using the significance analysis of microarrays (SAM) algorithm [J]. BMC Bioinformaties, 2005, 6 (1) : 129-135.
  • 4Zhou J, Foste D P, Stin R A, et al. Streamwise feature selection[J]. JMLR, 2006, 7 (9) : 1861-1885.
  • 5Zhang H, Wang H, Dai Z, et al. Improving accuracy for cancer classification with a new algorithm for genes selection [J]. BMC Bioinformatics, 2012, 13 (1) : 298-31 1.
  • 6Franken H, Lehmann R, H/iring H, et al. Wrapper and ensemble based feature subset selection methods for biomarker discovery in targeted metabolomics[M]//Pattern Recognition in Bioinformaties. Berlin: Springer, 2011,7036: 121-132.
  • 7Saeys Y, Abel T, Peer Y V. Robust feature selection using ensemble feature selection techniques [C]//Proceeding of the European Conference. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Berlin: Springer, 2008,5212: 313-325.
  • 8Anderson S P, Yoon L, Richard E B, et al. Delayed liver regeneration in peroxisome proliferator-activated receptor-alpha-null mice[J]. Hepatology, 2002, 36 (3) : 544-554.
  • 9Kouyama R, Suganami T, Nishida J. Attenuation of diet-induced weight gain and adiposity through increased energy expenditure in mice lacking angiotensin II type l a receptor[J]. Endocrinology, 2005, 146 (8):3481-3490.

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