MiRNN: A Mutual Information Augmented Recurrent Neural Network Framework for Reconstruction of Gene Regulatory Networks

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@InProceedings{dey:2024:CEC,

• author = "Prianka Dey and Abhinandan Khan and Goutam Saha and Rajat Kumar Pal",
• title = "{MiRNN:} A Mutual Information Augmented Recurrent Neural Network Framework for Reconstruction of Gene Regulatory Networks",
• booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2024",
• editor = "Bing Xue",
• address = "Yokohama, Japan",
• month = "30 " # jun # " - 5 " # jul,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Proteins, Recurrent neural networks, Accuracy, Escherichia coli, Evolutionary computation, Genetics, Bayes methods, gene regulatory network, mutual information, recurrent neural network, reverse engineering",
• isbn13 = "979-8-3503-0837-2",
• DOI = "doi:10.1109/CEC60901.2024.10612057",
• abstract = "Genes act as the blueprint for regulating all activities of a living system. Genes produce proteins, which in turn, sit on the promoter regions of other genes to regulate their activity. Thus, a gene regulatory network is formed. This network is critical in disclosing the various mysteries in the operations of living systems. Often it is very difficult to find these networks in the Wet Lab. As a result, various computational approaches have been used to reconstruct these networks from gene ex-pression data. The techniques primarily used for this purpose include Bayesian networks, Boolean networks, recurrent neural networks, S-systems, and mutual information based methods. The contemporary literature indicates that these techniques often fail to reliably reconstruct real-life networks. In this paper, we have proposed a new technique based on a modified recurrent neural network strategy that is augmented by mutual information. The proposed methodology has been implemented on an 8-gene network of Escherichia coli and a lO-gene network, which have been extensively used by other researchers. The experimental results indicate that the proposed technique achieves satisfactory results when compared to other such techniques developed by contemporary researchers.",
• notes = "also known as \cite{10612057}

  WCCI 2024",

}

Genetic Programming entries for Prianka Dey Abhinandan Khan Goutam Saha Rajat Kumar Pal

Citations