title: Analyzing large gene expression and methylation data profiles using StatBicRM: statistical biclustering-based rule mining.
abstract: Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data-matrix. Finally, we have also included the integrated analysis of gene expression and methylation for determining epigenetic effect (viz., effect of methylation) on gene expression level.
authors.affiliation: Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India.
authors.forename: Ujjwal
authors.initials: U
authors.lastname: Maulik
authors.affiliation: Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India.
authors.forename: Saurav
authors.initials: S
authors.lastname: Mallik
authors.affiliation: Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India.
authors.forename: Anirban
authors.initials: A
authors.lastname: Mukhopadhyay
authors.affiliation: Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India.
authors.forename: Sanghamitra
authors.initials: S
authors.lastname: Bandyopadhyay
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D019175:DNA Methylation; D057225:Data Mining / Q000379:methods*; D020869:Gene Expression Profiling
references.citation: BMC Med Res Methodol. 2005 Nov 03;5:35
references.pmid: 16269081
references.citation: Bioinformatics. 2005 Jul 1;21(13):3017-24
references.pmid: 15840707
references.citation: Bioinformatics. 2003 Jan;19(1):79-86
references.pmid: 12499296
references.citation: BMC Bioinformatics. 2006 Mar 10;7:126
references.pmid: 16529652
references.citation: Genome Biol. 2012 Dec 10;13(12):R112
references.pmid: 23228031
references.citation: Gynecol Oncol. 1999 Sep;74(3):375-80
references.pmid: 10479496
references.citation: Crit Care. 2004 Apr;8(2):130-6
references.pmid: 15025774
references.citation: Onco Targets Ther. 2012;5:171-6
references.pmid: 22973112
references.citation: PLoS One. 2008 Feb 20;3(2):e1651
references.pmid: 18297132
references.citation: J Clin Endocrinol Metab. 1993 May;76(5):1308-13
references.pmid: 8496322
references.citation: Bioinformatics. 2006 May 1;22(9):1122-9
references.pmid: 16500941
references.citation: IEEE/ACM Trans Comput Biol Bioinform. 2014 Jan-Feb;11(1):95-115
references.pmid: 26355511
references.citation: Lung Cancer. 2012 Aug;77(2):407-14
references.pmid: 22542705
references.citation: Nat Genet. 2005 Jun;37 Suppl:S31-7
references.pmid: 15920528
references.citation: PLoS One. 2012;7(3):e33284
references.pmid: 22428009
references.citation: PLoS One. 2012;7(4):e32289
references.pmid: 22539940
references.citation: BMC Genomics. 2011 Nov 30;12 Suppl 3:S24
references.pmid: 22369099
references.citation: Stat Methods Med Res. 2006 Feb;15(1):3-20
references.pmid: 16477945
references.citation: Stat Appl Genet Mol Biol. 2004;3:Article3
references.pmid: 16646809