2004 Breakout Sessions
C3 Breakout Session
View Synchronized Slides - Schaefer
View Synchronized Slides - Zupan
View Synchronized Slides - Ochs
Networks and Pathways
Dynamic Construction of Pathway Networks
Carl Schaefer, PhD
The prototype Pathway Interaction Database (National Cancer Institute, Center for Bioinformatics, http://cmap.nci.nih.gov/PW) allows the dynamic construction and visualization of pathway networks from a database of individual interactions. Networks can be specified by a combination of means: identifying pre-defined pathways of interest, identifying molecules of interest, and extending the initially selected set of interactions by including upstream and downstream interactions.
Inference of Genetic Networks from Mutant Phenotypes
Blaz Zupan, PhD
In the talk, we will show how genetic networks can be reconstructed from data on mutant phenotypes. The method employs em\xpert-defined patterns to uncover gene relations from the data, and uses these relations as constraints in the search for plausible genetic networks. We originally crafted the method for inference from morphological and other specific phenotypes, and are currently extending it to deal with gene expression phgenotypes. Both approaches, together with implementations within a Web-based system called GenePath (www.genepath.org) and a data mining suite called Orange (www.ailab.si/orange), will be presented.
Identifying Pathway Activity from High-Throughput Data
Michael Ochs, PhD
The cellular signaling pathways that control cell growth, differentiation, apoptosis, and motility play a critical role in many cancers. Microarray and proteomics technologies provide detailed information on mRNA and protein levels in vivo. However, because of the limited nature of our knowledge of signaling pathways in humans and high noise levels in the data, analysis is difficult. Here we present a Bayesian method to encode known biological knowledge in order to enhance the usefulness of expression and proteomics data for determination of signaling activity