We are a computational biology group interested in developing statistical machine learning methods to understand gene regulatory networks driving cellular functions. We are interested in identifying networks under different environmental, developmental, disease and evolutionary contexts and examining their dynamics. We ultimately aim to construct predictive models from these molecular networks that can inform us how the system will behave under different perturbations. We develop tools for bulk and single cell omic datasets and apply them to diverse biological and biomedical questions with the central theme of understanding gene regulation. Learn more about our research.

The Latest at Roy Lab

Computational Tools Unlock Evolutionary Complexity

With a new computational approach led by Roy lab member Junha Shin, publicly-available gene expression datasets, and the new data, Roy and her collaborators were able to connect changes in protein levels to phenotypic traits.

Siahpirani paper earns top 10 at RECOMB/ISCB 2017

Congratulations to Alireza Siahpirani! His paper A prior-based integrative framework for functional transcriptional regulatory network inference earned a top 10 rating at RECOMB/ISCB November 19-21, 2017 in New York City, NY. ISCB is the leading professional society for computational biology and bioinformatics. Check out Siahpirani's paper and the other top rated papers.

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