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Researchers at Carnegie Mellon University have developed a new dynamic statistical model to visualize changing patterns in networks, including gene expression during developmental periods of the brain.
Published in the Proceedings of the National Academy of Sciences, the model now gives researchers a tool that extends past observing static networks at a single snapshot in time, which is hugely beneficial since network data are usually dynamic. The analysis of network data—or the study of relationships from a large-scale view—is an emerging field of statistics and data science.
Dataset - Component - People - Way - Communities
"For any dataset with a dynamic component, people can now use this in a powerful way to find communities that persist and change over time," said Kathryn Roeder, the UPMC Professor of Statistics and Life Sciences in the Dietrich College of Humanities and Social Sciences. "This will be very helpful in understanding how certain diseases and disorders progress. For example, we know that certain genes are responsible for autism and can use our model to give us insight into at what point the disorder begins developing."
The model, Persistent Communities by Eigenvector Smoothing (PisCES), combines information across a series of networks, longitudinally, to strengthen the inference for each period. The CMU team used PisCES to follow neural gene expressions from conception through adulthood in rhesus monkey brains to find out what genes work together during different points of development.
Visualization - Method - Combines - Tools - Community
"Our visualization method combines two different existing tools: community detection, which is a popular tool for static network data, and sankey plots, which are often used to visualize 'flows' of information. Our visualization organizes the actors of the network into communities that evolve over time and then depicts the evolving community memberships as a series of flows between...
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