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面向时序基因表达数据的双聚类算法 被引量:3

Bicluster algorithm facing time-series gene expression data
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摘要 对某种生物而言,在某段连续时间内共表达的基因预示着其在同时完成某一生物过程或其间存在某种调控关系;而目前在基因表达数据上的大多数双聚类算法都是针对非连续样本点的情况提出的,对于连续样本点(样本之间存在顺序关系)的情况很少涉及。因此在考虑连续样本点的情况下,提出了一种在时序基因表达数据上挖掘极大一致趋势共表达基因集的双聚类算法TCBicluster。在每个时间点产生行常量共表达基因集,进而构造以时间点为顶点、以相邻时间点间满足一致性要求的共表达基因集为边的权值图,并采用扩展连续时间点的方式对权值图进行双聚类挖掘,使用有效的剪枝策略提高算法效率。实验证明,TCBicluster算法比RAP及CC-TSB算法更能有效挖掘极大一致趋势共表达双聚类且具有较高的效率和良好的可扩展性。 For one creature, if some genes on it show co-expressed in a certain continuous time interval, they are very likely to complete a biological process simultaneously or exist some regulation relationships. At present, most of the bicluster algo- rithms in gene expression data were proposed under the discontinuous samples. That is, the bicluster algorithms for samples existing a sequential relationship were very few. For this reason, this paper proposed an efficient time-continuous bicluster al- gorithm TCBieluster to mine the maximal coherent evolution and co-expression gene sets from the time-series microarray gene expression dataset. First, TCBicluster algorithm generated all the constant row co-expression gene sets for every time point. Then, it built the weighted range multigraph which used the time points as its vertexes and the co-expression gene sets with co- herent evolution between two adjacent time points as its edges. Finally, TCBicluster expanded the multigraph with a mode that only considered the behind adjacent vertex as the candidate. In addition, it used some efficient pruning techniques to improve the efficiency. The experimental results show that the maximal coherent evolution and co-expression biclusters mined by TCBi- cluster algorithm are of better quality than RAP and CC-TSB. Simultaneously, TCBicluster algorithm also indicates higher mining efficiency and better extensibility.
出处 《计算机应用研究》 CSCD 北大核心 2013年第8期2308-2314,共7页 Application Research of Computers
基金 国家"973"计划资助项目(2012CB316203) 国家自然科学基金资助项目(61272121)
关键词 时间点连续 基因共表达 一致趋势 双聚类 time-continuous gene co-expression coherent evolution bicluster
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参考文献14

  • 1TAVAZOIE S, HUGHES J D, CAMPBELL M J, et al. Systematic determination of genetic network architecture [ J]. Nature Genetics, 1999,22 ( 3 ) :281 - 285.
  • 2RAMONI M, SEBASTIANI P, KOHANE I. Cluster analysis of gene expression dynamics [ J ]. Proceedings of the National Academy of Sciences of the USA,2002,99(14) :9121-9126.
  • 3CHENG Yi-zhong, CHURCH G M. Biclustering of expression data [ C ]//Proc of the 8th International Conference on Intelligent Systems for Molecular Biology. New York : ACM Press, 2000:93-103.
  • 4BEN-DOR A, CHOR B, KARP R, et al. Discovering local structure in gene expression data: the order-preserving submatrix problem [ C l//Proc of the 6th Annual International Conference on Computa- tional Biology. New York: ACM Press,2002:49-57.
  • 5CHENG K O, LAW N F, SIU W C, et al. BiVisu: software tool for bicluster detection and visualization [ J ]. Biointormatics, 2007,23 ( 17 ) :2342-2344.
  • 6ZHAO Li-zhuang, ZAKI M J. MicroCluster: an efficient deterministic biclustering algorithm for microarray data[ J]. IEEE Intelligent Sys- tems,2005,20(6) :40-49.
  • 7PANDEY G, ATLURI G, STEINBACH M, et al. An association a- nalysis approach to biclnsting[ C ]//Proc of the 15th ACM Conference on Kownlege Discovery and Data Mining. New York: ACM Press, 2009 : 677 - 686.
  • 8ZHANG Ya, ZHA Hong-yuan, CHU C H. A time-series biclustering algorithm for revealing co-regulated genes[ C]//Proc of the 5th IEEE International Conference on Information Technology : Coding and Com- puting. Washington DC: IEEE Computer Society,2005:32-37.
  • 9王淼,尚学群,谢华博,等.行常量差异表达基因模式挖掘算法研究[J].计算机研究与发展,2012,49(增刊):228-234.
  • 10WANG Miao, SHANG Xue-qun, MIAO Miao, et al. MSPattem : effi- cient mining maximal subspace differential co-expression patterns in microarray dat asets[ C ]//Proc of IEEE International Conference on Signal Processing, Communication and Computing. 2011 : 181 - 190.

同被引文献43

  • 1殷爱茹,李栋,黄亚楼.基因表达数据聚类有效性分析的EFOM法[J].计算机工程与应用,2005,41(17):53-55. 被引量:4
  • 2闫雷鸣,孙志挥.一种基于二次互信息的双聚类算法[J].计算机工程与应用,2006,42(22):158-160. 被引量:4
  • 3RAMONI M,SEBASTIANI P,KOHANE I.Cluster analysis of gene ex-pression dynamics[J].PNAS,2002,99(14):9121-9126.
  • 4MADEIRA S C,OLIVEIRA A L.Biclustering algorithms for biologicaldata analysis:a survey[J].IEEE/ACM Trans on ComputationalBiology and Bioinformatics,2004,1(1):24-45.
  • 5TANAY A,SHARAN R,SHAMIR R.Discovering statistically signifi-cant biclusters in gene expression data[J].Bioinformatics,2002,18(SI):136-144.
  • 6BEN-DOR A,CHOR B,KARP R,ef al.Discovering local structure ingene expression data:the order-preserving submatrix problem[J].Journal of Computational Biology,2003,10(3-4):373-384.
  • 7MURALI T M,KASIF S.Extracting conserved gene expression motifsfrom gene expression data[C]//Proc of the 8th Pacific Symposium onBiocomputing.2003:77-88.
  • 8CHENG Y1CHURCH G M.Biclustering of expression data[C]//Procof International Conference on Intelligent Systems for MolecularBiology.New York:ACM Press,2000:93-103.
  • 9ZHAO Li-zhuang,ZAKI M.MicroCluster:efficient deterministicbiclustering of microarray data[J].IEEE Intelligent Systems,2005,20(6):40-49.
  • 10PANDEY G,ATLURI G,STEINBACH M,et al An association analy-sis approach to biclustering[C]//Proc of the 15th ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining.New York:ACM Press,2009:677-686.

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