Genes are segments of DNA that provide a blueprint for cells and organisms to effectively control processes and regulations within individuals. There have been many attempts to quantify these processes, as a greater understanding of how genes operate could have large impacts on both personalized and precision medicine. Current biological methods cannot easily reveal the details of gene interactions. Therefore, we use gene expression data to infer networks of interactions, which are called gene regulatory networks or GRNs. These methods are designed to bypass the need for large amounts of data and extensive knowledge about a network. In this work, we extend previous work by investigating additional ways to incorporate stochasticity into gene regulatory networks.





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