Machine Learning Methods for Gene Regulatory Network Inference

Proper regulation of gene expression is essential to ensure that genes are activated only when necessary and that their activity is properly controlled [3]. The regulation of gene expression is achieved through understanding the intricate interactions between genes and other molecules. In this effort, Gene Regulatory Networks have emerged as a strong tool[2]. Gene regulatory networks (GRNs) are complex systems that determine the development, differentiation, and function of cells and organisms, as well as their response to environmental stimuli [4][5]. GRNs consist of genes, transcription factors (TFs), microRNAs, and other regulatory molecules that interact with each other to control gene expression [6]. The regulatory interactions between these molecules can form complex networks that exhibit emergent properties, such as robustness and adaptability [7]. In its simplest form, a GRN is a network of genes and their regulatory interactions, which govern the expression of these genes in response to various cellular cues. It is worth noting that in this definition, a transcription factor (TF) is considered a special kind of gene that may regulate the expression of other non-TF or TF genes. Each gene in the network acts as a node, and the regulatory interactions between genes are represented by directed edges connecting these nodes[8].