11/9/2023 0 Comments Process lasso license keyFor example, characterizing the connection between genotype and phenotype and, furthermore, pathology, requires not only the identification of the molecules involved in the process and their specific characteristics but also a description of the ways in which these molecules interact with each other across spatiotemporal scales. As such, all biological systems, especially molecular systems, are inherently complex, and the global structure and behavior of the system cannot be straightforwardly inferred from the (local) properties of its components. In complex system theory, a system is defined as complex if certain properties, such as nonlinearity, feedback loops, adaptation, and nontrivial behavior, emerge from the collective interactions between the system components and the surrounding environment. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches.
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