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

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...

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Bibliographic Details
Published in:BioSystems Vol. 236; p. 105126
Main Authors: da Silva, José Eduardo H., de Carvalho, Patrick C., Camata, José J., de Oliveira, Itamar L., Bernardino, Heder S.
Format: Journal Article
Language:English
Published: Ireland Elsevier B.V 01.02.2024
Subjects:
Cartesian Genetic Programming
Data distribution
Discretization
Gene expression data
Gene regulatory network
Gene regulatory network
Data distribution
Gene expression data
Discretization
Cartesian Genetic Programming
ISSN:0303-2647, 1872-8324, 1872-8324
Online Access:Get full text
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Summary: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.
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ISSN:0303-2647
1872-8324
1872-8324
DOI:10.1016/j.biosystems.2024.105126