MiRNN: A Mutual Information Augmented Recurrent Neural Network Framework for Reconstruction of Gene Regulatory Networks
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- @InProceedings{dey:2024:CEC,
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author = "Prianka Dey and Abhinandan Khan and Goutam Saha and
Rajat Kumar Pal",
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title = "{MiRNN:} A Mutual Information Augmented Recurrent
Neural Network Framework for Reconstruction of Gene
Regulatory Networks",
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booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2024",
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editor = "Bing Xue",
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address = "Yokohama, Japan",
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month = "30 " # jun # " - 5 " # jul,
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publisher = "IEEE",
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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",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10612057",
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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.",
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notes = "also known as \cite{10612057}
WCCI 2024",
- }
Genetic Programming entries for
Prianka Dey
Abhinandan Khan
Goutam Saha
Rajat Kumar Pal
Citations