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Knowledge-driven data integration for the prediction of protein-protein interaction networks

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Zheng, Huiru, Browne, Fiona, Wang, Haiying and Azuaje, Francisco (2008) Knowledge-driven data integration for the prediction of protein-protein interaction networks. In: 16th Annual International Conference Intelligent Systems for Molecular Biology . UNSPECIFIED. 1 pp. [Conference contribution]

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Abstract

Information about the networks of protein interactions within a cell can greatly increase our understanding of protein function and cellular processes. The advent of high- throughput experimental techniques, as well as large-scale computational prediction models, has greatly increased the volume of genomic data aimed at discovering the functionality of genes and proteins. Individually, these data should be viewed with caution as they are often inaccurate and incomplete. Recent years have seen a growing trend towards the adoption of diverse data integration techniques to support large-scale analysis of protein-protein interaction (PPI) networks. However, current research is mainly focused on data-driven approaches. This paper proposes a knowledge-driven computational framework to support systems-level data integration for the prediction of PPI networks. Based on the incorporation of prior knowledge of the relationship between different “omic” datasets, different likelihood-ratio-based Bayesian models (LR-NB) have been developed to combine the evidence from diverse sources, ranging from co- expression to essentiality to formulate PPI predictions. We demonstrate improvements in the PPI prediction performance. Results are evaluated against Gold Standards, which were derived from the MIPS Complex Catalogue (Saccharomyces cerevisiae) and the Human Protein Reference Database. We also implement a novel analysis of local regions of a Receiver Operating Characteristic curve as less biased and more exact approach to assessing the quality of prediction models. This investigation also provides the basis for new PPI network inference and analysis applications in other model organisms and in specific diseases.

Item Type:Conference contribution (Poster)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute > Artificial Intelligence and Applications
Computer Science Research Institute > Smart Environments
ID Code:11478
Deposited By:Dr Huiru Zheng
Deposited On:08 Feb 2010 16:50
Last Modified:08 Feb 2010 16:50

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