NDLI: Functional and promoter enrichment based analysis of biclustering algorithms using gene expression data of yeast

Content Provider	IEEE Xplore Digital Library
Author	Noureen, N. Kulsoom, N. de la Fuente, A. Fazal, S. Malik, S.I.
Copyright Year	2009
Description	Author affiliation: CRS4 Bioinformatica c/o Parco Tecnologico, Edificio 1, Loc. Piscina Manna, 09010 PULA (CA), Italy (de la Fuente, A.) \|\| Department of Bioinformatics, Mohammad Ali Jinnah University Jinnah Avenue Blue Area Islamabad, Pakistan (Noureen, N.; Kulsoom, N.; Fazal, S.; Malik, S.I.)
Abstract	Biclustering algorithms are an important tool for the analysis of gene expression data. Research on analysis of gene expression data includes identification of groups of genes with similar expression patterns. Standard clustering methods for the analysis of gene expression data only identify the global similarity while missing the local patterns of expression similarity, i.e. genes could behave similar over only a subset of the observations. In order to identify such patterns biclustering approaches have been introduced. This paper compares the performance of five important biclustering methods by applying them on a gene expression dataset of yeast. The dataset of Saccharomyces cerevisiae comprised of 5736 genes expressed in 112 strains of yeast. The performance is scored based on the algorithm's ability to find functionally enriched as well as transcription factor target site enriched groups of genes. Our studies shows that among the chosen five biclustering algorithms SAMBA and ISA showed the best performance on the basis of functional enrichment. Biclusters were also obtained through remaining three algorithms also but they were not functionally enriched.
Starting Page	1
Ending Page	6
File Size	204767
Page Count	6
File Format	PDF
ISBN	9781424448722
DOI	10.1109/INMIC.2009.5383144
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-14
Publisher Place	Pakistan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Gene expression Fungi Clustering algorithms Iterative algorithms Data analysis Instruction sets Performance analysis Bioinformatics Pattern analysis Saccharomyces cerevisiae Biclusters clustering local patterns Global patterns functional enrichment promoter enrichment
Content Type	Text
Resource Type	Article

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