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DNF: A differential network flow method to identify rewiring drivers for gene regulatory networks

What is being modeled?
Driver genes of development and diseases
Description & purpose of resource

Differential network analysis has become an important approach in identifying driver genes in development and disease. However, most studies capture only local features of the underlying gene-regulatory network topology. These approaches are vulnerable to noise and other changes which mask driver-gene activity. Therefore, methods are urgently needed which can separate the impact of true regulatory elements from stochastic changes and downstream effects. We propose the differential network flow (DNF) method to identify key regulators of progression in development or disease. Given the network representation of consecutive biological states, DNF quantifies the essentiality of each node by differences in the distribution of network flow, which are capable of capturing comprehensive topological differences from local to global feature domains.

Spatial scales
Temporal scales
1 - 103 s
weeks to months
This resource is currently
mature and useful in ongoing research
Has this resource been validated?
Can this resource be associated with other resources? (e.g.: modular models, linked tools and platforms)
Key publications (e.g. describing or using resource)

Xie, Jiang, et al. "DNF: A differential network flow method to identify rewiring drivers for gene regulatory networks." Neurocomputing 410 (2020): 202-210.

Qing Nie
PI contact information
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