Student: Jennifer Staigar

School: Rutgers University, Math Dept.


Research Area: Mathematical Biology

Project Title: Tensor Decomposition of Microarray Data

Faculty Mentor: Dr. Stanley Dunn, Professor of Biomedical Engineering

Project Description:

The accomplishments of modern biology, highlighted by completion of sequencing of the Human Genome, are remarkable and have the potential to lead to unprecedented advances in biotechnology and in health care. The molecular "parts list" of cells and tissues is growing at an accelerating pace, and a major issue limiting the application of this data is the ability to integrate information on the parts into an understanding of the whole. These needs give rise to a set of activities that we term Molecular Systems Bioengineering (MSB). MSB represents an engineering approach to the understanding and control of biological processes. It encompasses high-throughput microarray data acquisition techniques on genomes, proteomes and other molecular catalogs, and it involves the use of both data-driven and principles-driven modeling techniques to create an understanding of biological phenotype based on a combination of molecular catalogs and environmental conditions. The goal of this research is to investigate empirical, or data-driven methods for analyzing DNA microarrays. The aim is to explore data-driven rather than theoretical methods for elucidating regulation network structure from microarrays. Empirical methods, which have been studied in areas such as machine learning, computer vision and cognitive science, have not been widely studied in the context of microarray analysis. Boosting, bootstrapping and factor space methods will be considered as techniques to develop algorithms for identifying genes clustered by expression as well as identifying regulatory relationships. We will also investigate empirical methods for: statistical analysis of empirical algorithm performance results; empirical comparison of different algorithms; methods / tools / databases for empirical performance evaluation; standardization and independent testing; and design of empirical evaluation methods and protocols. This research program will lead to methods for evaluating algorithms as well as understanding of the role of empirical methods for microarray analysis. 


Project Results from Spellman et al. Data:

Project Results from Chu et al. Data:

Project Presentations:

First Presentation:  Click here to see Power Point slides presented on June 24, 2005
Final Presentation:  Click here to see Power Point slides presented on July 21, 2005  

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