Abstract
This paper investigates applying a genetic algorithm and an evolutionary programming for identification of gene interaction networks from gene expression data. To this end, we employ recurrent neural networks to model gene interaction networks and make use of an artificial gene expression data set from literature to validate the proposed approach. We find that the proposed approach using the genetic algorithm and evolutionary programming can result in better parameter estimates compared with the other previous approach. We also find that any a priori knowledge such as zero relations between genes can further help the identification process whenever it is available.
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Jung, S.H., Cho, KH. (2005). Identification of Gene Interaction Networks Based on Evolutionary Computation. In: Kim, T.G. (eds) Artificial Intelligence and Simulation. AIS 2004. Lecture Notes in Computer Science(), vol 3397. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30583-5_46
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DOI: https://doi.org/10.1007/978-3-540-30583-5_46
Publisher Name: Springer, Berlin, Heidelberg
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