Inference of Gene Regulatory Networks from Bulk and Single Cell Omic Datasets
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore gene regulatory network inference techniques from bulk and single-cell omic datasets in this comprehensive lecture from the Computational Genomics Summer Institute 2022. Delve into the importance of regulatory networks, experimental mapping techniques, and various inference paradigms. Learn about expression-based network inference principles, integrative approaches, and prior-based methods. Examine predictive models of expression and their applications in identifying regulators of early lineage commitment and transcriptional dynamics during cellular reprogramming. Discover how single-cell genomics is revolutionizing biology and the incorporation of accessibility data in single-cell GRN inference. Gain insights into specific algorithms and methodologies, including Inferelator, Modified Elastic Net (MEN), MERLIN+P, and Symphony, while exploring related research papers for further understanding.