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New Carnegie Mellon Dynamic Statistical Model Follows Gene Expressions Over Time


The new dynamic statistical model to visualize changing patterns in networks.

Carnegie Mellon University researchers say they have developed a new dynamic statistical model to visualize changing patterns in networks, including gene expression during developmental periods of the brain.

Credit: Carnegie Mellon News

Researchers at Carnegie Mellon University (CMU) say they have developed a new dynamic statistical model called Persistent Communities by Eigenvector Smoothing (PisCES) to visualize changing patterns in networks, including gene expression during developmental periods of the brain.

"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," says CMU professor Kathryn Roeder.

PisCES longitudinally integrates data across a series of networks to fortify the inference for each period, and the team employed PisCES to follow neural gene expressions from conception through adulthood in rhesus monkey brains.

The researchers say PisCES analysis pointed to the existence of change points and periods of persistent gene community structure, including a dynamic community of genes involved in neural projection guidance that was very active during the mid to late fetal period.

The team wants the model to be applied to social networks and other relational situations.

From Carnegie Mellon News
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