|
Activity Number:
|
81
|
|
Type:
|
Invited
|
|
Date/Time:
|
Monday, July 30, 2007 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Biometrics Section
|
| Abstract - #307814 |
|
Title:
|
Self-Correcting Maps of Molecular Pathways
|
|
Author(s):
|
Andrey Rzhetsky and Tian Zheng*+ and Chani Weinreb
|
|
Companies:
|
Columbia University and Columbia University and Columbia University
|
|
Address:
|
Department of Statistics, New York, NY, 10027,
|
|
Keywords:
|
bayesian network ; molecular network ; conflicting data ; text-mining
|
|
Abstract:
|
Reliable and comprehensive maps of molecular pathways are indispensable for guiding complex biomedical experiments. Such maps are typically assembled from myriads of disparate research reports and are replete with inconsistencies due to variations in experimental conditions and/or errors. It is often an intractable task to manually verify internal consistency over a large collection of experimental statements. To automate large-scale reconciliation efforts, we propose a random-arcs-and-nodes model where both nodes (tissue-specific states of biological molecules) and arcs (interactions between them) are represented with random variables. We show how to obtain a noncontradictory model of a molecular network by computing the joint distribution for arc and node variables, and then apply our methodology to a realistic network, generating a set of experimentally testable hypotheses.
|
