Inferring Gene Regulatory Networks from Single-Cell RNA-Sequencing Experimental Data using Cartesian Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{da-silva:2024:CEC,

• author = "Jose Eduardo H. {da Silva} and Heder S. Bernardino and Itamar L. {de Oliveira} and Jose J. Camata and Patrick {de Carvalho}",
• title = "Inferring Gene Regulatory Networks from Single-Cell {RNA}-Sequencing Experimental Data using Cartesian Genetic Programming",
• 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, Cartesian Genetic Programming, Computational modeling, Systems biology, Evolutionary computation, Prediction algorithms, Inference algorithms, Data models, Gene Regulatory Network, Experimental Data, Boolean Models",
• isbn13 = "979-8-3503-0837-2",
• DOI = "doi:10.1109/CEC60901.2024.10611826",
• 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