CAREER: Machine Learning Approaches for Genome-wide Biological Network Interference
Project Award Date: 02-22-2007
Description
Because of technological limitations, molecular biology research has had to focus on individual genes and gene products. This has led to a wealth of knowledge about individual cellular components and their functions. Isolated cellular components are not sufficient to understand most cellular functions, which are carried out by complex networks. It is therefore imperative to employ network-based approaches to address the complexity of living systems.
Scientists in life-science research must find how to computationally model and elucidate complex networks from high-throughput biological data sets. Thus, this research focuses on developing and applying novel computational methods for reconstructing genome-wide biological networks from high-throughput data.
ITTC researchers will develop and apply novel computational approaches for uncovering networks of interactions between genes and proteins. They will conduct related educational activities in a newly established bioinformatics program in the Department of Electrical Engineering and Computer Science at the University of Kansas. A wide-range of students, from high school through graduate school, will receive special training opportunities in the interdisciplinary area of bioinformatics.
ITTC Investigator Xue-wen Chen will develop machine learning methods for effectively integrating multiple prior knowledge from different data sources, highly heterogeneous data learning, and large-scale network learning. Learning with prior knowledge and highly heterogeneous data sources are fundamental to computational biology, information theory, machine learning, data mining, and other areas. The research will produce new methods and user-friendly software for molecular biologists.
Investigators
Faculty Investigator(s): Xue-wen Chen (PI)
Student Investigator(s): Mei Liu, Jong Jeong, Bing Han, Michael Wasikowski, Jae Kim, Alexander Senf, Matthew Mandelbaum, Patrick Dermyer
Project Sponsors
Primary Sponsor(s): NSF
