New Carnegie Mellon Dynamic Statistical Model Follows Gene Expressions Over Time

Future applications could range from fMRI data to social networks. 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 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. "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.