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Using Predictive models to identify regulators of early lineage commitment
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Classroom Contents
Inference of Gene Regulatory Networks from Bulk and Single Cell Omic Datasets
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- 1 Intro
- 2 What is a regulatory network
- 3 Why do regulatory networks matter?
- 4 Expperimental techniques for mapping regulatory network components
- 5 Paradigms of network inference
- 6 Expression-based network inference basic principle
- 7 A non-exhaustive list of expression-based network inference method
- 8 Milestones in expression-based GRN inference
- 9 Integrative expression-based network inference
- 10 Prior-based approaches for network inference
- 11 Methods for incorporating auxiliary data
- 12 Inferelator: A parameter prior-based network inference algorithm
- 13 Modified Elastic Net (MEN)
- 14 MERLIN+P: A structure prior-based network inference algorithm
- 15 Network component analysis (NCA) for TFA estimation
- 16 A non-exhaustive list of integrative network inference methods
- 17 Basic principle of predictive models of expression
- 18 Inferring GRNs using predictive models of expression
- 19 Using Predictive models to identify regulators of early lineage commitment
- 20 Transcriptional dynamics during cellular reprogramming
- 21 Expression-based GRN inference vs Predictive models of expression
- 22 Single cell genomics is revolutionizing biology
- 23 Classes of network inference algorithms
- 24 Incorporating accessibility for single cell GRN inference
- 25 Symphony
