Inferring Gene Regulatory Networks from Single-Cell RNA-Sequencing Experimental Data using Cartesian Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{da-silva:2024:CEC,
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author = "Jose Eduardo H. {da Silva} and Heder S. Bernardino and
Itamar L. {de Oliveira} and Jose J. Camata and
Patrick {de Carvalho}",
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title = "Inferring Gene Regulatory Networks from Single-Cell
{RNA}-Sequencing Experimental Data using Cartesian
Genetic Programming",
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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, Cartesian
Genetic Programming, Computational modeling, Systems
biology, Evolutionary computation, Prediction
algorithms, Inference algorithms, Data models, Gene
Regulatory Network, Experimental Data, Boolean Models",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10611826",
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abstract = "Systems Biology is an interdisciplinary field that
aims to understand the interactions among biological
components. A central focus of this field is modelling
gene regulatory networks (GRN) and understanding how
gene expression varies. ScRNA -Seq technology has
enabled the ability to explore gene expression at the
single-cell level, unlike previous technologies where
only an average view of gene expression was possible.
As a result, the literature has observed a significant
increase in the number of inference methods, taking
into account the specificities of the data from scRNA
-Seq profiling, such as batch effects, biological
variations, and dropouts. However, recent studies have
shown that the performance of GRN inference algorithms
when considering scRNA -Seq technology is close to
random predictors. Furthermore, algorithms that perform
well on synthetic and curated data are different from
those that perform well on experimental data,
indicating a lack of robustness. Considering that
experimental data is more interesting for biology, as
the modelling of its GRNs enables the understanding of
biological phenomena, in this paper we show that the
CGPGRN framework can deal with experimental data.
Computational experiments are carried out and the
results indicate that CGPGRN can outperform
state-of-the-art algorithms in several situations and
is the only one capable of obtaining correct regulatory
relationships in all situations considered.",
-
notes = "also known as \cite{10611826}
WCCI 2024",
- }
Genetic Programming entries for
Jose Eduardo Henriques da Silva
Heder Soares Bernardino
Itamar Leite de Oliveira
Jose J Camata
Patrick de Carvalho
Citations