Inferring Time-Varying Network Topologies from Gene Expression Data
Author Information
Author(s): Arvind Rao, Hero Alfred O III, States David J, Engel James Douglas
Primary Institution: University of Michigan
Hypothesis
Current methods for gene regulatory network identification lead to the inference of steady-state networks, which may not accurately represent dynamic cellular states.
Conclusion
The proposed regime-SSM approach effectively infers time-varying gene regulatory networks, demonstrating conformity with experimental evidence.
Supporting Evidence
- The approach was tested on mouse embryonic kidney and T-cell activation datasets.
- The results align with previously reported experimental evidence.
Takeaway
This study shows how to understand gene networks that change over time, which is important for seeing how genes interact in different situations.
Methodology
The study uses a clustering method based on underlying dynamics followed by system identification using a state-space model to infer a network adjacency matrix.
Digital Object Identifier (DOI)
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