@InProceedings{6952937,
  author = {B. Abu-Jamous and R. Fa and D. J. Roberts and A. K. Nandi},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Bi-CoPaM ensemble clustering application to five Escherichia coli bacterial datasets},
  year = {2014},
  pages = {2485-2489},
  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;biological processes;microarray datasets;Escherichia coli bacterial datasets;Microorganisms;Genomics;Bioinformatics;Biological processes;Gene expression;Filtering;Bi-CoPaM;microarray data analysis;gene clustering;Escherichia coli bacteria},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569923243.pdf},
}

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K."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["B."],"propositions":[],"lastnames":["Abu-Jamous"],"suffixes":[]},{"firstnames":["R."],"propositions":[],"lastnames":["Fa"],"suffixes":[]},{"firstnames":["D.","J."],"propositions":[],"lastnames":["Roberts"],"suffixes":[]},{"firstnames":["A.","K."],"propositions":[],"lastnames":["Nandi"],"suffixes":[]}],"booktitle":"2014 22nd European Signal Processing Conference (EUSIPCO)","title":"Bi-CoPaM ensemble clustering application to five Escherichia coli bacterial datasets","year":"2014","pages":"2485-2489","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. 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