DocumentCode :
149777
Title :
Bi-CoPaM ensemble clustering application to five Escherichia coli bacterial datasets
Author :
Abu-Jamous, Basel ; Rui Fa ; Roberts, David J. ; Nandi, A.K.
Author_Institution :
Dept. of Electron. & Comput. Eng., Brunel Univ., Uxbridge, UK
fYear :
2014
fDate :
1-5 Sept. 2014
Firstpage :
2485
Lastpage :
2489
Abstract :
Bi-CoPaM ensemble clustering has the ability to mine a set of microarray datasets collectively to identify the subsets of genes consistently co-expressed in all of them. It also has the capability of considering the entire gene set without pre-filtering as it implicitly filters out less interesting genes. While it showed success in revealing new insights into the biology of yeast, it has never been applied to bacteria. In this study, we apply Bi-CoPaM to five bacterial datasets, identifying two clusters of genes as the most consistently co-expressed. Strikingly, their average profiles are consistently negatively correlated in most of the datasets. Thus, we hypothesise that they are regulated by a common biological machinery, and that their genes with unknown biological processes may be participating in the same processes in which most of their genes known to participate. Additionally, our results demonstrate the applicability of Bi-CoPaM to a wide range of species.
Keywords :
genomics; lab-on-a-chip; Escherichia coli bacterial datasets; biological processes; microarray datasets; Bioinformatics; Biological processes; Filtering; Gene expression; Genomics; Microorganisms; Bi-CoPaM; Escherichia coli bacteria; gene clustering; microarray data analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European
Conference_Location :
Lisbon
Type :
conf
Filename :
6952937
Link To Document :
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