Abstract
Gene Regulatory Network (GRN) contains interactions occurring between transcription factors (TF) and target genes which are captured during the microarray creation. However, information about the interactions among microRNAs (miRNA) and target genes can not be captured by current microarray technology. To overcome this limitation, we propose a new technique to reverse engineer GRN from partial microarray data which represent target genes’ interactions only. Using S-System model, the approach is modified to incorporate the unavailability of information about miRNA-target genes interactions. The most versatile Differential Evolutionary algorithm is used for optimization and parameter learning. Experimental studies on three newly created synthetic networks, and one real network of Saccharomyces cerevisiae called IRMA network, show significant improvement compared to traditional S-System based approach.
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Chowdhury, A.R., Chetty, M., Vinh, X.N. (2012). On the Reconstruction of Genetic Network from Partial Microarray Data. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34475-6_83
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DOI: https://doi.org/10.1007/978-3-642-34475-6_83
Publisher Name: Springer, Berlin, Heidelberg
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