Conference proceeding
Estimating support for protein-protein interaction data with applications to function prediction
Computational systems bioinformatics. Computational Systems Bioinformatics Conference, Vol.7, pp.73-84
2008
DOI: 10.1142/9781848162648_0007
PMID: 19642270
Abstract
Almost every cellular process requires the interactions of pairs or larger complexes of proteins. High throughput protein-protein interaction (PPI) data have been generated using techniques such as the yeast two-hybrid systems, mass spectrometry method, and many more. Such data provide us with a new perspective to predict protein functions and to generate protein-protein interaction networks, and many recent algorithms have been developed for this purpose. However, PPI data generated using high throughput techniques contain a large number of false positives. In this paper, we have proposed a novel method to evaluate the support for PPI data based on gene ontology information. If the semantic similarity between genes is computed using gene ontology information and using Resnik's formula, then our results show that we can model the PPI data as a mixture model predicated on the assumption that true protein-protein interactions will have higher support than the false positives in the data. Thus semantic similarity between genes serves as a metric of support for PPI data. Taking it one step further, new function prediction approaches are also being proposed with the help of the proposed metric of the support for the PPI data. These new function prediction approaches outperform their conventional counterparts. New evaluation methods are also proposed.
Details
- Title: Subtitle
- Estimating support for protein-protein interaction data with applications to function prediction
- Creators
- Erliang Zeng - Bioinformatics Research Group, School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA. ezeng001@cs.fiu.eduChris DingGiri NarasimhanStephen R Holbrook
- Resource Type
- Conference proceeding
- Publication Details
- Computational systems bioinformatics. Computational Systems Bioinformatics Conference, Vol.7, pp.73-84
- Publisher
- United States
- DOI
- 10.1142/9781848162648_0007
- PMID
- 19642270
- ISSN
- 1752-7791
- Grant note
- S06 GM008205 / NIGMS NIH HHS P01 DA15027-01 / NIDA NIH HHS
- Language
- English
- Date published
- 2008
- Academic Unit
- Preventive and Community Dentistry; Roy J. Carver Department of Biomedical Engineering; Iowa Neuroscience Institute; Biostatistics; Dental Research
- Record Identifier
- 9984071937002771
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