Oct 17, 2012

Ruan receives $452K NSF grant for bioinformatics




The National Science Foundation has awarded a 3-year $452,657 grant to support PI Jianhua Ruan in research on III: Small: Topology-based approaches to integrated analysis of transcriptomic, protein interactomic and phenotypic data. Dr. Ruan is an assistant professor in the Department of Computer Science at the University of Texas at San Antonio.

 

High-throughput experimental technologies that allow biologists to measure tens of thousands of cellular components simultaneously have revolutionized biology and are opening new doors to systems-level understanding and manipulation of biology. However, how to integrate these heterogeneous and noisy measurements and to model the relationships among the different components poses critical challenges for both biologists and computational scientists. This project aims at developing efficient and effective computational algorithms and tools to integrate multiple high-throughput data within the framework of biological networks, with the following specific objectives. First, this project will develop algorithms to improve network quality and network module discovery using information embedded in network topology. Second, this research will develop computational methods to systematically investigate the relationship between network topology and biological functions, which is expected to advance the current understanding of the organizing principles of biological networks, and facilitate prioritizing genes in disease studies. Finally, this project proposes a novel algorithm for identifying potential causal genes associated with cancer phenotypes, and a novel similarity metric to compare patients based on pathway/subnetwork-level gene expression patterns, which can be easily combined with existing clustering / classification algorithms for network-based prediction of cancer outcomes.

 

The final outputs of this project will include both bioinformatics tools for integrative data analysis and databases of biological knowledge discovered from different input datasets. These tools and resources will be made freely available on the web, which can be used by a broad range of researchers who are interested in bioinformatics algorithm development or applications. These tools and resources will be demonstrated on a number of applications that have the promises to improve crop yield, prognosis and treatment of breast cancer, or design of anti-cancer drugs.