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Seminar | Data Science and Learning Division

Inference and Analysis of Transcriptional Regulatory Networks: Background, Methods, and Recent Work

DSL Seminar

Abstract: Inference of transcriptional regulatory networks (TRNs) is one of the critical tasks in systems biology. Experiments are now producing data in quantities large enough for researchers to attempt to reconstruct such networks based primarily on correlations in gene expression, aided by other types of high-throughput data (e.g., ATAC-seq). This talk will introduce some of the algorithms for such network reconstruction and results from the DREAM conferences that have publicly tested such algorithms. I will also present some of the latest algorithm work, my testing of such, give some observations on the promise and difficulties in the use of high-throughput single-call data for inferring TRNs and inferring markers of cellular state, and briefly look at topological (graph) analysis of such networks.