Abstract
This paper presents a simple and novel approach involving the aggregation of some correlation-based techniques for deciphering simple gene interaction sub-networks from biclusters in microarray time series gene expression data. Preprocessing has been used for discarding the weakly interacting gene pairs, i.e., those that are poorly correlated. The proposed technique was successfully applied to public-domain data sets of Yeast and the experimental results were biologically validated based on benchmark databases and information from literature.
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Das, R., Mitra, S. (2012). Aggregation of Correlation Measures for the Reverse Engineering of Gene Regulatory Sub-networks. In: Kundu, M.K., Mitra, S., Mazumdar, D., Pal, S.K. (eds) Perception and Machine Intelligence. PerMIn 2012. Lecture Notes in Computer Science, vol 7143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27387-2_30
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DOI: https://doi.org/10.1007/978-3-642-27387-2_30
Publisher Name: Springer, Berlin, Heidelberg
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