Assessing and Predicting Protein Interactions Using Both Local and Global Network Topological Metrics
High-throughput protein interaction data, with ever-increasing volume, are becoming the foundation of many biological discoveries. However, high-throughput protein interaction data are often associated with high false positive and false negative rates. It is desirable to develop scalable methods to...
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| Published in | Genome Informatics Vol. 21; pp. 138 - 149 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Japan
Japanese Society for Bioinformatics
2008
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0919-9454 2185-842X |
| DOI | 10.11234/gi1990.21.138 |
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| Abstract | High-throughput protein interaction data, with ever-increasing volume, are becoming the foundation of many biological discoveries. However, high-throughput protein interaction data are often associated with high false positive and false negative rates. It is desirable to develop scalable methods to identify these errors. In this paper, we develop a computational method to identify spurious interactions and missing interactions from high-throughput protein interaction data. Our method uses both local and global topological information of protein pairs, and it assigns a local interacting score and a global interacting score to every protein pair. The local interacting score is calculated based on the common neighbors of the protein pairs. The global interacting score is computed using globally interacting protein group pairs. The two scores are then combined to obtain a final score called LGTweight to indicate the interacting possibility of two proteins. We tested our method on the DIP yeast interaction dataset. The experimental results show that the interactions ranked top by our method have higher functional homogeneity and localization coherence than existing methods, and our method also achieves higher sensitivity and precision under 5-fold cross validation than existing methods. |
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| AbstractList | High-throughput protein interaction data, with ever-increasing volume, are becoming the foundation of many biological discoveries. However, high-throughput protein interaction data are often associated with high false positive and false negative rates. It is desirable to develop scalable methods to identify these errors. In this paper, we develop a computational method to identify spurious interactions and missing interactions from high-throughput protein interaction data. Our method uses both local and global topological information of protein pairs, and it assigns a local interacting score and a global interacting score to every protein pair. The local interacting score is calculated based on the common neighbors of the protein pairs. The global interacting score is computed using globally interacting protein group pairs. The two scores are then combined to obtain a final score called LGTweight to indicate the interacting possibility of two proteins. We tested our method on the DIP yeast interaction dataset. The experimental results show that the interactions ranked top by our method have higher functional homogeneity and localization coherence than existing methods, and our method also achieves higher sensitivity and precision under 5-fold cross validation than existing methods. |
| Author | Li, Jinyan Liu, Guimei Wong, Limsoon |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19425154$$D View this record in MEDLINE/PubMed |
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J., Efficient Mining of Frequent Patterns Using Ascending Frequency Ordered Prefix-Tree. Data Mining and Knowledge Discovery, 9 (3): 249-274, 2004. [4] Chen J, Hsu W, Lee ML, and Ng SK, Discovering reliable protein interactions from high-throughput experimental data using network topology. Artificial Intelligence in Medicine, 35 (1-2): 37-47, 2005. [27] Tan SH., Hugo W., Sung WK., and Ng SK., A correlated motif approach for finding short linear motifs from protein interaction networks. BMC Bioinformatics, 7: 502, 2006. [5] Chen J, Hsu W, Lee ML, and Ng SK, Increasing confidence of protein interactomes using network topological metrics. Bioinformatics, 22 (16): 1998-2004, 2006. [28] von Mering C, Krause R, Snel B, Cornell M, Oliver SG, Fields S, and Bork P, Comparative assessment of large-scale data sets of protein-protein interactions. Nature, 417: 399-403, 2002. [29] Yu H, Paccanaro A, Trifonov V, and Gerstein M, Predicting interactions in protein networks by completing defective cliques. Bioinformatics, 22 (7): 823-829, 2006. [23] Ramani AK., Bunescu RC., Mooney RJ., and Marcotte EM., Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome. Genome Biology, 6 (5): R40, 2005. [11] Goh CS, Bogan AA, Joachimiak M, Walther D, and Cohen FE., Co-evolution of proteins with their interaction partners. Journal of Molecular Biology, 299 (2): 283-93, 2000. [14] Kim WK, Park J, and Suh JK, Large scale statistical prediction of protein-protein interaction by potentially interacting domain (pid) pair. Genome Informatics Series: Workshop on Genome Informatics, 13: 42-50, 2002. [24] Saito R, Suzuki H, and Hayashizaki Y, Interaction generality, a measurement to assess the reliability of a protein-protein interaction. Nucleic Acids Research, 30 (5): 1163-1168, 2002. [26] Sprinzak E, Sattath S, and Margalit H, How reliable are experimental protein-protein interaction data? Journal of Molecular Biology, 327 (5): 919-923, 2003. [25] Saito R, Suzuki H, and Hayashizaki Y, Construction of reliable protein-protein interaction networks with a new interaction generality measure. Bioinformatics, 19 (6): 756-763, 2002. [2] Brun C, Chevenet F, Martin D, Wojcik J, Guenoche A, and Jacq B, Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network. Genome Biology, 5 (1): R6, 2003. |
| References_xml | – reference: [4] Chen J, Hsu W, Lee ML, and Ng SK, Discovering reliable protein interactions from high-throughput experimental data using network topology. Artificial Intelligence in Medicine, 35 (1-2): 37-47, 2005. – reference: [7] Chua, HN., Sung, WK., and Wong L., An efficient strategy for extensive integration of diverse biological data for protein function prediction. Bioinformatics, 3 (24): 3364-3373, 2007. – reference: [19] Morrison JL, Breitling R, Higham DJ, and Gilbert DR, A lock-and-key model for protein-protein interactions. Bioinformatics, 22 (16): 2012-2019, 2006. – reference: [22] Pazos F, and Valencia A., Similarity of phylogenetic trees as indicator of proteinprotein interaction. Protein Engineering, 14 (9): 609-14, 2001. – reference: [18] Marcotte EM., Pellegrini M., Ng HL., Rice DW., Yeates TO., and Eisenberg D., Detecting protein function and protein-protein interactions from genome sequences. Science, 285 (5428): 751-3m, 1999. – reference: [6] Chua HN., Sung WK., and Wong L., Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions. Bioinformatics, 22 (13): 1623-30, 2006. – reference: [14] Kim WK, Park J, and Suh JK, Large scale statistical prediction of protein-protein interaction by potentially interacting domain (pid) pair. Genome Informatics Series: Workshop on Genome Informatics, 13: 42-50, 2002. – reference: [28] von Mering C, Krause R, Snel B, Cornell M, Oliver SG, Fields S, and Bork P, Comparative assessment of large-scale data sets of protein-protein interactions. Nature, 417: 399-403, 2002. – reference: [9] Deane CM., Salwinski L., Xenarios I., and Eisenberg D., Protein interactions: two methods for assessment of the reliability of high throughput observations. Mol Cell Proteomics., 1 (5): 349-56, 2002. – reference: [23] Ramani AK., Bunescu RC., Mooney RJ., and Marcotte EM., Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome. Genome Biology, 6 (5): R40, 2005. – reference: [2] Brun C, Chevenet F, Martin D, Wojcik J, Guenoche A, and Jacq B, Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network. Genome Biology, 5 (1): R6, 2003. – reference: [16] Li H, Li J, and Wong L, Discovery motif pairs at interaction sites from protein sequences on a proteome-wide scale. Bioinformatics, 22 (8): 989-996, 2006. – reference: [17] Liu G., Lu H., Lou W., Xu Y., Yu X. J., Efficient Mining of Frequent Patterns Using Ascending Frequency Ordered Prefix-Tree. Data Mining and Knowledge Discovery, 9 (3): 249-274, 2004. – reference: [29] Yu H, Paccanaro A, Trifonov V, and Gerstein M, Predicting interactions in protein networks by completing defective cliques. Bioinformatics, 22 (7): 823-829, 2006. – reference: [15] Legrain P, Wojcik J, and Gauthier JM, Protein-protein interaction maps: a lead towards cellular functions. Trends in genetics, 17 (6): 346-352, 2001. – reference: [3] Chen J, Chua HN, Hsu W, Lee ML, Ng SK, Saito R, Sung WK, and Wong L, Increasing confidence of protein-protein inteactomes. In Proc. of 17th International Conference on Genome Informatics, pp.284-297, 2006. – reference: [11] Goh CS, Bogan AA, Joachimiak M, Walther D, and Cohen FE., Co-evolution of proteins with their interaction partners. Journal of Molecular Biology, 299 (2): 283-93, 2000. – reference: [26] Sprinzak E, Sattath S, and Margalit H, How reliable are experimental protein-protein interaction data? Journal of Molecular Biology, 327 (5): 919-923, 2003. – reference: [27] Tan SH., Hugo W., Sung WK., and Ng SK., A correlated motif approach for finding short linear motifs from protein interaction networks. BMC Bioinformatics, 7: 502, 2006. – reference: [10] Edwards AM, Kus B, Jansen R, Greenbaum D, Greenblatt J, and Gerstein M, Bridging structural biology and genomics: assessing protein interaction data with known complexes. Trends in Genetics, 18 (10): 529-536, 2002. – reference: [21] Oliver S, Proteomics: guilt-by-association goes global. Nature, 403: 601-603, 2000. – reference: [24] Saito R, Suzuki H, and Hayashizaki Y, Interaction generality, a measurement to assess the reliability of a protein-protein interaction. Nucleic Acids Research, 30 (5): 1163-1168, 2002. – reference: [13] Han D, Kim HS, Seo J, and Jang W, A domain combination based probabilistic framework for protein-protein interaction prediction. Genome Informatics Series: Workshop on Genome Informatics, 14: 250-259, 2003. – reference: [20] Ng SK, Zhang Z, and Tan SH, Integrative approach for computationally inferring protein domain interactions. Bioinformatics, 19 (8): 923-929, 2003. – reference: [1] Bock JR, and Gough DA, Predicting protein-protein interactions from primary structure. Bioinformatics, 17 (5): 455-460, 2001. – reference: [12] Gomez SM, and Rzhetsky A, Towards the prediction of complete protein-protein interaction networks. In Pacific Symposium on Biocomputing, pp.413-424, 2002. – reference: [5] Chen J, Hsu W, Lee ML, and Ng SK, Increasing confidence of protein interactomes using network topological metrics. Bioinformatics, 22 (16): 1998-2004, 2006. – reference: [25] Saito R, Suzuki H, and Hayashizaki Y, Construction of reliable protein-protein interaction networks with a new interaction generality measure. Bioinformatics, 19 (6): 756-763, 2002. – reference: [8] Dandekar, T., Snel, B., Huynen, M., and Bork, P., Conservation of gene order: a fingerprint of proteins that physically interact. Trends in Biochemical Sciences, 23 (9): 324-8, 1998. |
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| SubjectTerms | Algorithms False Negative Reactions False Positive Reactions Fungal Proteins - chemistry Fungal Proteins - genetics Fungal Proteins - metabolism Kinetics Models, Genetic Models, Theoretical network topology Predictive Value of Tests protein-protein interaction Proteins - chemistry Proteins - genetics Proteins - metabolism Reproducibility of Results Yeasts - genetics Yeasts - metabolism |
| Title | Assessing and Predicting Protein Interactions Using Both Local and Global Network Topological Metrics |
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