DocumentCode :
2780744
Title :
A comparison of biclustering algorithms
Author :
Verma, Nishchal K. ; Meena, Sheela ; Singh, Ashutosh ; Cui, Yan ; Bajpai, Shruti ; Nagrare, Aditya
Author_Institution :
Dept. of Electr. Eng., Indian Inst. of Technol., Kanpur, India
fYear :
2010
fDate :
16-18 Dec. 2010
Firstpage :
90
Lastpage :
97
Abstract :
In the past years, various microarray technologies have been used to extract useful biological information from microarray data. Microarray technologies have become a central tool in biological research. The extraction or identification of gene groups with similar expression pattern, plays an important role in the analysis of genes. The primary techniques involve clustering and biclustering methods. Besides classical clustering methods, biclustering is being preferred to analyze biological datasets, due to its ability to group both genes across conditions simultaneously. Biclustering is being practiced in a number of applications to club genes across specified conditions, used mainly in identifying sets of coregulated genes, tissue classification etc. Gene Ontology is another important area of application, where biclusters are used to presume the class of non-annotated genes. Gene Ontology database is competent of annotating and analyzing a large number of genes. Gene Ontology is a standard approach of representing the gene with their product attributes, across different species and databases. Typical annotations for the analyzed list of genes can be well understood using the BicAT and BiVisu toolbox. The toolbox provides a platform which enables us to compare different biclustering algorithms, inside the graphical tool. This paper compares different biclustering approaches used to analyze carcinoma and DLBCL (diffuse large B-cell lymphoma) microarray datasets. The algorithms were compared on the grounds of enrichment values with support from runtime analysis. The paper explains in detail the biclusters associated with each algorithm and the intellects affecting the enrichment values, leading to the best biclustering technique for the datasets mentioned above.
Keywords :
bioinformatics; cancer; genetics; genomics; BiVisu toolbox; BicAT toolbox; Gene Ontology; biclustering algorithm; biological research; carcinoma; diffuse large B-cell lymphoma; gene expression pattern; gene group; microarray technology; tissue classification; Algorithm design and analysis; Biology; Pipelines; BicAT; Biclustering; Gene Ontology; Microarray;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems in Medicine and Biology (ICSMB), 2010 International Conference on
Conference_Location :
Kharagpur
Print_ISBN :
978-1-61284-039-0
Type :
conf
DOI :
10.1109/ICSMB.2010.5735351
Filename :
5735351
Link To Document :
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