This project is motivated by the pressing need for analyzing modern high-dimensional heterogeneous data from multiple sources. Driven by high-throughput biotechnologies, it is increasingly common to have multiple types of measurements on the same set of subjects. Integration of heterogeneous data types is the key to gaining fundamental knowledge on biological processes. Complex characteristics of modern biological data also result in challenges for data analysis and statistical modeling. This project will contribute to theoretical and methodological development through novel graphical models suitable for fusing correlated and mixed data from static and dynamic conditions. These approaches have great potential to translate rich data into meaningful knowledge. The new statistical methods and theories will also advance modern statistical science. The resulting products of this project will provide valuable software tools to scientific communities. This research will promote curriculum development, student training, and educational outreach.
Multimodal data from different experimental platforms have different properties and characteristics. In many systems, including biological processes, regulation is modularized and temporally dynamic in nature. Common regulatory principles exist in certain related biological conditions. This project will focus on the development of new data integration methods and theories through novel correlated graphical models from a frequentist inference perspective. The methods will be based on exponential Markov random fields beyond the traditional Gaussian assumption. The new methods will allow for network discovery for high-dimensional heterogeneous data from multiple static and dynamic conditions. This project also involves the development of efficient algorithms for complex optimization problems incorporating the structure-inducing regularization mechanism.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
