Probabilistic inference of molecular networks from noisy data sources

Iossifov, I., Krauthammer, M., Friedman, C., Hatzivassiloglou, V., Bader, J. S., White, K. P., Rzhetsky, A. (May 2004) Probabilistic inference of molecular networks from noisy data sources. Bioinformatics, 20 (8). pp. 1205-13. ISSN 1367-4803 (Print)1367-4803 (Linking)

DOI: 10.1093/bioinformatics/bth061


Information on molecular networks, such as networks of interacting proteins, comes from diverse sources that contain remarkable differences in distribution and quantity of errors. Here, we introduce a probabilistic model useful for predicting protein interactions from heterogeneous data sources. The model describes stochastic generation of protein-protein interaction networks with real-world properties, as well as generation of two heterogeneous sources of protein-interaction information: research results automatically extracted from the literature and yeast two-hybrid experiments. Based on the domain composition of proteins, we use the model to predict protein interactions for pairs of proteins for which no experimental data are available. We further explore the prediction limits, given experimental data that cover only part of the underlying protein networks. This approach can be extended naturally to include other types of biological data sources.

Item Type: Paper
Uncontrolled Keywords: Algorithms Cell Physiological Phenomena Database Management Systems Databases, Bibliographic Databases, Protein Information Storage and Retrieval/ methods Models, Biological Models, Statistical Periodicals as Topic Protein Interaction Mapping/ methods Sequence Analysis, Protein/methods Signal Transduction/ physiology Stochastic Processes Two-Hybrid System Techniques Yeasts/metabolism
Subjects: bioinformatics
CSHL Authors:
Communities: CSHL labs > Iossifov lab
Depositing User: Matt Covey
Date: 22 May 2004
Date Deposited: 01 Apr 2015 18:54
Last Modified: 01 Apr 2015 18:54
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