A Biclustering Algorithm Based on PA Index in Microarray Gene Expression Data

doi:10.3969/j.issn.1006-2475.2014.12.003

Abstract

Abstract: To improve the global quality of the outcome of the biclustering, a biclustering algorithm based on PA index was proposed. In this algorithm, the PA index which can estimate the effect of outcome of the biclustering was chosen to construct the bicluster model. Through deleting or inserting rows and columns in the heuristic greedy fashion, the algorithm obtained the significant biclusters of high global quality. To compare the performance of the algorithm with CC and FLOC, a real dataset experiment was simulated. The result shows that the algorithm in this paper can obtain better results. In a word, the algorithm is capable of detecting potentially statistically and biologically significant biclusters.

Key words: PA index, biclustering, GO analysis, microarray gene expression data

CLC Number:

TP391

LIN Qin1, LIN Si-da2, ZHU Wen-min1. A Biclustering Algorithm Based on PA Index in Microarray Gene Expression Data[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2014.12.003.

References

[1] Cheng Yizong, Church G M. Biclustering of expression data[C]// Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology. 2000:93-103.

[2] Yang Jiong, Wang Wei. Enhanced biclustering on gene expression data[C]// Proceedings of the 3rd IEEE Conference on Bioinformatics and Bioengineering. 2003:321-327.

[3] 朱娴,许建华. 基于模拟退火粒子群优化的基因数据双聚类算法[J]. 计算机与应用化学, 2013,30(1):93-96.

[4] 王常武,刘楠楠,贾永伟,等. 应用于癌症基因表达数据的OMB双向聚类算法[J]. 计算机工程与应用, 2011,47(28):237-240.

[5] 胡云,苗夺谦,王睿智,等. 一种基于粗糙k均值的双聚类算法[J]. 计算机科学, 2007,34(11):174-177.

[6] 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.

[7] Zhang Ya, Zha Hongyuan, Chu C H. A time-series biclustering algorithm for revealing co-regulated genes[C]// Proceedings of the 5th IEEE International Conference on Information Technology: Coding and Computing. 2005:32-37.

[8] 杨蜜静,尚学群,许涛,等. 面向时序基因表达数据的双聚类算法[J]. 计算机应用研究, 2013,30(8):2308-2314.

[9] 林勤,薛云. 双聚类算法在电信高价值客户细分的应用[J]. 计算机应用, 2014,34(6):1807-1811.

[10] Ashburner M, Ball C A, Blake J A, et al. Gene ontology: Tool for the unification of biology[J]. Nature Genetics, 2000,25(1):25-29.

[11] David Martin, Christine Brun, Elisabeth Remy, et al. GOToolBox: Functional analysis of gene datasets based on gene ontology[J]. Genome Biology, 2004,5(12):R101.

[12] Zeeberg B R, Feng Weimin, Wang G, et al. GoMiner: A resource for biological interpretation of genomic and proteomic data[J]. Genome Biology, 2003,4(4):R28.

[13] Fadhl Al-Akwaa, Yasser Kadah. An automatic gene ontology software tool for bicluster and cluster comparisons[C]// Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. 2009.

[14] Shamir R, Maron-Katz A, Tanay A, et al. Expander: An integrative program suite for microarray data analysis[J]. BMC Bioinformatics, 2005,6: Article Number 232.

[15] Barkow S, Bleuler S, Prelic A, et al. BicAT: A biclustering analysis toolbox[J]. Bioinformatics, 2006,22(10):1282-1283.

[16] Mitra S, Banka H. Multi-objective evolutionary biclustering of gene expression data[J]. Pattern Recognition, 2006,39(12):2464-2477.

[17] Liu Junwan, Li Zhoujun, Liu Feifei, et al. Multi-objective particle swarm optimization biclustering of microarray data[C]// Proceedings of the 2008 IEEE International Conference on Bioinformatics and Biomedicine. 2008:363-366.