The interplay of interactions between DNA, RNA and proteins leads to genetic regulatory networks (GRN) and in turn controls the gene regulation. Directly or indirectly in a cell such molecules either interact in a positive or in repressive manner therefore it is hard to obtain the accurate computational models through which the final state of a cell can be predicted with certain accuracy. This paper describes biological behaviour of actual regulatory systems and we propose a novel method for GRN discovery of a large number of genes from multiple time series gene expression observations over small and irregular time intervals. The method integrates a genetic algorithm (GA) to select a small number of genes and a Kaiman filter to derive the GRN of these genes. After GRNs of smaller number of genes are obtained, these GRNs may be integrated in order to create the GRN of a larger group of genes of interest. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Kasabov, N. K., Chan, Z. S. H., Jain, V., Sidorov, I., & Dimitrov, D. S. (2004). Gene regulatory network discovery from time-series gene expression Data - A computational intelligence approach. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3316, 1344–1353. https://doi.org/10.1007/978-3-540-30499-9_209
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