GenExplore: Interactive Exploration of
[Research Task] [People] [Papers]
Microarray data provides a powerful basis for analysis
of gene expression. Data mining methods such as clustering have been widely
applied to microarray data to link genes that show similar expression patterns.
However, this approach usually fails to unveil multiple interactions by the same
gene. In this project we aim to apply statistical models (Graphical Gaussian
Models, Graphical Loglinear Models) and data mining techniques (Hierarchical
Clustering, Association Rules) to discover gene interactions (pairwise and
multi-way levels). We aim to construct a prototype system that permits rapid
interactive exploration of gene relationships; results can be validated by
experts or domain information, or suggest new experiments.
- Develop a framework for interactive, scalable
and exploratory gene interaction analysis.
- Investigate various interaction analysis
techniques to discover gene interactions and derive genetic networks. The
- graphical gaussian modeling to discover
undirected pairwise linear gene interactions for subsets with a relatively large number of genes.
- graphical decomposition techniques
to get components with a relatively small number of genes.
- graphical loglinear modeling to discover
multi-way gene interactions within each component.
- causal modeling to discover directed pairwise
- Investigate various visualization techniques for
interactive gene interactions.
- Apply system to study prostate cancer and
X.Wu, Y. Ye. "Exploring Gene Causal Interactions
Using an Enhanced Constraint-based Method", Pattern Recognition
Y.Ye, X.Wu "Efficient Causal Interaction Learning
with Applications in Microarray",
Y.Ye, X. Wu, K. Subramanian and L. Zhang. "GenExplore:
Interactive Exploration of Gene Interactions from Microarray Data",
ICDE 2004 (demo) .
X. Wu, Y. Ye and L. Zhang. "Graphical Modeling Based Gene Interaction
Analysis for Microarray Data", SIGKDD Explorations, 5(2) 91-100,
- X. Wu, D. Barbara, Y. Ye. "Screening and
Interpreting Multi-item Associations Based on Log-linear Modeling", SIGKDD2003.
- X. Wu, D. Barbara, L. Zhang and Y. Ye. "Gene
Interaction Analysis using All k-way Interaction Loglinear: A Case Study on Yeast Data",
ICML03 workshop: Machine
Learning in Bioinformatics.
X. Wu, Y. Ye, K. Subramanian and L. Zhang.
"Interactive Gene Interaction Analysis Using Graphical Gaussian Model".
beta version is available.
Some research projects, papers and products can be found here