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Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions
Published on: August 2, 2015
Inferring high-confidence human protein-protein interactions
Xueping Yu1, Anders Wallqvist, Jaques Reifman
1Biotechnology High-Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Ft. Detrick, MD 21702, USA.
BMC Bioinformatics
|May 8, 2012
Summary
This study introduces a novel unsupervised statistical method to reliably score human protein-protein interactions (PPIs). This approach effectively reduces noise and enhances the accuracy of high-confidence PPI networks for better biological insights.
Area of Science:
- Bioinformatics
- Computational Biology
- Systems Biology
Background:
- Protein-protein interactions (PPIs) data aggregation is prone to experiment-dependent noise.
- Distinguishing reliable PPIs from noisy data is challenging due to many interactions being reported only once.
- Existing methods struggle to rank PPIs effectively, hindering the creation of high-confidence networks.
Purpose of the Study:
- To develop an unsupervised statistical approach for scoring and ranking experimentally identified human PPIs.
- To create high-confidence human PPI networks by reducing noise from aggregated data.
- To evaluate the proposed ranking method against existing approaches using independent reference sets.
Main Methods:
- An unsupervised statistical method was developed to score PPIs from nine primary databases.
- The ranking method was evaluated by comparing its ability to retrieve known protein associations against reference sets.
- Performance was quantified by comparing enrichment percentages against random ranking, hypergeometric test, and occurrence ranking.
Main Results:
- The proposed method achieved a ~134% enrichment in retrieving high-confidence PPIs, outperforming hypergeometric test (~109%) and occurrence ranking (~46%).
- Higher-ranked PPIs demonstrated a greater likelihood of reflecting high-confidence experimental data.
- Noise reduction through the ranking scheme significantly improved the accuracy and enrichment of PPI data.
Conclusions:
- Ranked protein-protein interactions are valuable for identifying high-confidence biological data.
- The developed ranking scheme effectively reduces noise in aggregated PPI data, enhancing network accuracy.
- Utilizing these high-confidence PPIs at various confidence levels can elucidate topological and biological properties of human protein networks.
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