A Data-Distribution and Successive Spline Points based discretization approach for evolving gene regulatory networks from scRNA-Seq time-series data using Cartesian Genetic Programming
Created by W.Langdon from
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- @Article{DASILVA:2024:biosystems,
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author = "Jose Eduardo H. {da Silva} and
Patrick C. {de Carvalho} and Jose J. Camata and
Itamar L. {de Oliveira} and Heder S. Bernardino",
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title = "A Data-Distribution and Successive Spline Points based
discretization approach for evolving gene regulatory
networks from sc{RNA-Seq} time-series data using
Cartesian Genetic Programming",
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journal = "Biosystems",
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volume = "236",
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pages = "105126",
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year = "2024",
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ISSN = "0303-2647",
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DOI = "doi:10.1016/j.biosystems.2024.105126",
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URL = "https://www.sciencedirect.com/science/article/pii/S030326472400011X",
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keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming, Gene regulatory network,
Discretization, Data distribution, Gene expression
data",
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abstract = "The inference of gene regulatory networks (GRNs) is a
widely addressed problem in Systems Biology. GRNs can
be modeled as Boolean networks, which is the simplest
approach for this task. However, Boolean models need
binarized data. Several approaches have been developed
for the discretization of gene expression data (GED).
Also, the advance of data extraction technologies, such
as single-cell RNA-Sequencing (scRNA-Seq), provides a
new vision of gene expression and brings new challenges
for dealing with its specificities, such as a large
occurrence of zero data. This work proposes a new
discretization approach for dealing with scRNA-Seq
time-series data, named Distribution and Successive
Spline Points Discretization (DSSPD), which considers
the data distribution and a proper preprocessing step.
Here, Cartesian Genetic Programming (CGP) is used to
infer GRNs using the results of DSSPD. The proposal is
compared with CGP with the standard data handling and
five state-of-the-art algorithms on curated models and
experimental data. The results show that the proposal
improves the results of CGP in all tested cases and
outperforms the state-of-the-art algorithms in most
cases",
- }
Genetic Programming entries for
Jose Eduardo Henriques da Silva
Patrick C de Carvalho
Jose J Camata
Itamar Leite de Oliveira
Heder Soares Bernardino
Citations