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<p>Gene regulatory networks (GRNs) are one of the most promising techniques for modeling and understanding biological processes. GRNs help to understand complex interactions involving genes. Regulator molecules control gene expression within these networks through interactions with each other and other substances in cells. They play a crucial role in deciphering regulatory relationships among genes and modeling changes in gene expression under various conditions. In recent years, there has been a surge in the generation of vast quantities and diverse varieties of gene expression data. This bulk production has created a demand for new data management methods. It is expected that integrative analysis of various types of gene information will provide a comprehensive overview for researching complex biological systems and processes. This review covers recent developments related to Gene Regulatory Networks and associated gene expression data from 2019 to 2023. Key topics are presented according to the data type used for network building, machine learning approaches, tools used in network inference, model optimization, and computational validation techniques. This paper presents an in-depth technical review of the techniques currently used for constructing GRNs, explores possibilities for future research, and aims to enhance the understanding of GRNs.</p>

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A review of methods for gene regulatory networks reconstruction and analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月29日

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  • Sura I. Mohammed Ali,
  • Sura Zaki Alrashid

摘要

Gene regulatory networks (GRNs) are one of the most promising techniques for modeling and understanding biological processes. GRNs help to understand complex interactions involving genes. Regulator molecules control gene expression within these networks through interactions with each other and other substances in cells. They play a crucial role in deciphering regulatory relationships among genes and modeling changes in gene expression under various conditions. In recent years, there has been a surge in the generation of vast quantities and diverse varieties of gene expression data. This bulk production has created a demand for new data management methods. It is expected that integrative analysis of various types of gene information will provide a comprehensive overview for researching complex biological systems and processes. This review covers recent developments related to Gene Regulatory Networks and associated gene expression data from 2019 to 2023. Key topics are presented according to the data type used for network building, machine learning approaches, tools used in network inference, model optimization, and computational validation techniques. This paper presents an in-depth technical review of the techniques currently used for constructing GRNs, explores possibilities for future research, and aims to enhance the understanding of GRNs.