Abstract
A large number of genome-scale networks, including protein–protein and genetic interaction networks, are now available for several organisms. In parallel, many studies have focused on analyzing, characterizing, and modeling these networks. Beyond investigating the topological characteristics such as degree distribution, clustering coefficient, and average shortest-path distance, another area of particular interest is the prediction of nodes (genes) with a given characteristic (labels) – for example prediction of genes that cause a particular phenotype or have a given function. In this chapter, we describe methods and algorithms for predicting node labels from network-based datasets with an emphasis on label propagation algorithms (LPAs) and their relation to local neighborhood methods.
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References
Marcotte, E.M., et al., Detecting protein function and protein-protein interactions from genome sequences. Science, 1999. 285(5428): p. 751–3.
Wu, X., et al., Network-based global inference of human disease genes. Mol Syst Biol, 2008. 4: p. 189.
Aerts, S., et al., Gene prioritization through genomic data fusion. Nat Biotechnol, 2006. 24(5): p. 537–44.
Sharan, R., I. Ulitsky, and R. Shamir, Network-based prediction of protein function. Mol Syst Biol, 2007. 3: p. 88.
Oti, M. and H.G. Brunner, The modular nature of genetic diseases. Clin Genet, 2007. 71(1): p. 1–11.
Ashburner, M., et al., Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 2000. 25(1): p. 25–9.
Ogata, H., et al., KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res, 1999. 27(1): p. 29–34.
Ruepp, A., et al., The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucleic Acids Res, 2004. 32(18): p. 5539–45.
Robinson, P.N., et al., The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am J Hum Genet, 2008. 83(5): p. 610–5.
Hamosh, A., et al., Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res, 2005. 33(Database issue): p. D514–7.
Chua, H.N., W.K. Sung, and L. Wong, Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions. Bioinformatics, 2006. 22(13): p. 1623–30.
Zhou, X., M.C. Kao, and W.H. Wong, Transitive functional annotation by shortest-path analysis of gene expression data. Proc Natl Acad Sci USA, 2002. 99(20): p. 12783–8.
Myers, C.L., et al., Discovery of biological networks from diverse functional genomic data. Genome Biol, 2005. 6(13): p. R114.
Karaoz, E., et al., Protective role of melatonin and a combination of vitamin C and vitamin E on lung toxicity induced by chlorpyrifos-ethyl in rats. Exp Toxicol Pathol, 2002. 54(2): p. 97–108.
Deng, M., et al., Prediction of protein function using protein-protein interaction data. J Comput Biol, 2003. 10(6): p. 947–60.
Nabieva, E., et al., Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics, 2005. 21 Suppl 1: p. i302–10.
Mostafavi, S., et al., GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol, 2008. 9 Suppl 1: p. S4.
Tsuda, K., H. Shin, and B. Scholkopf, Fast protein classification with multiple networks. Bioinformatics, 2005. 21 Suppl 2: p. ii59-65.
Murali, T.M., C.J. Wu, and S. Kasif, The art of gene function prediction. Nat Biotechnol, 2006. 24(12): p. 1474–5; author reply 1475–6.
Deng, M., T. Chen, and F. Sun, An integrated probabilistic model for functional prediction of proteins. J Comput Biol, 2004. 11(2–3): p. 463–75.
Mostafavi, S. and Q. Morris, Fast Integration of Heterogeneous Data Sources for Predicting Gene Function with Limited Annotation. Bioinformatics, 2010.
Lanckriet, G.R., et al., A statistical framework for genomic data fusion. Bioinformatics, 2004. 20(16): p. 2626–35.
Pena-Castillo, L., et al., A critical assessment of Mus musculus gene function prediction using integrated genomic evidence. Genome Biol, 2008. 9 Suppl 1: p. S2.
Pavlidis, P., et al., Learning gene functional classifications from multiple data types. J Comput Biol, 2002. 9(2): p. 401–11.
Zhang, B. and S. Horvath, A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol, 2005. 4: p. Article17.
Yona, G., et al., Effective similarity measures for expression profiles. Bioinformatics, 2006. 22(13): p. 1616–22.
Warde-Farley, D., et al., The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res, 2010. Accepted(Webserver Issue).
Costanzo, M., et al., The genetic landscape of a cell. Science, 2010. 327(5964): p. 425–31.
Tong, A.H., et al., Global mapping of the yeast genetic interaction network. Science, 2004. 303(5659): p. 808–13.
Weirauch, M.T., et al., Information-based methods for predicting gene function from systematic gene knock-downs. BMC Bioinformatics, 2008. 9: p. 463.
Hishigaki, H., et al., Assessment of prediction accuracy of protein function from protein--protein interaction data. Yeast, 2001. 18(6): p. 523–31.
Schwikowski, B., P. Uetz, and S. Fields, A network of protein-protein interactions in yeast. Nat Biotechnol, 2000. 18(12): p. 1257–61.
Zhou, D., et al., Learning with Local and Global Consistency, in Neural Information Processing Systems. 2003, MIT Press: Vancouver, BC, Canada.
Weston, J., et al., Protein ranking: from local to global structure in the protein similarity network. Proc Natl Acad Sci USA, 2004. 101(17): p. 6559–63.
Hu, P., H. Jiang, and A. Emili, Predicting protein functions by relaxation labelling protein interaction network. BMC Bioinformatics, 2010. 11 Suppl 1: p. S64.
Bengio, Y., O. Delalleau, and N. Le Roux, Label Propagation and Quadratic Criterion, in Semi-Supervised Learning, O. Chapelle, B. Scholkopf, and A. Zien, Editors. 2006, MIT Press.
Chung, F., Spectral Graph Theory. Number 92 in CBMS Regional Conference Series in Mathematics. 1999: American Mathematical Society.
Vazquez, A., et al., Global protein function prediction from protein-protein interaction networks. Nat Biotechnol, 2003. 21(6): p. 697–700.
Karaoz, U., et al., Whole-genome annotation by using evidence integration in functional-linkage networks. Proc Natl Acad Sci USA, 2004. 101(9): p. 2888–93.
Fraser, A.G. and E.M. Marcotte, A probabilistic view of gene function. Nat Genet, 2004. 36(6): p. 559–64.
Lee, I., et al., A probabilistic functional network of yeast genes. Science, 2004. 306(5701): p. 1555–8.
Myers, C.L. and O.G. Troyanskaya, Context-sensitive data integration and prediction of biological networks. Bioinformatics, 2007. 23(17): p. 2322–30.
Huttenhower, C., et al., Exploring the human genome with functional maps. Genome Res, 2009. 19(6): p. 1093–106.
Noble, W.S. and A. Ben-Hur, Integrating Information for Protein Function Prediction, in Bioinformatics-From Genomes to Therapies, T. Lengauer, Editor. 2007, Wiley-VCH Verlag GmbH & Co KGaA: Weinheim, Germany.
Song, J. and M. Singh, How and when should interactome-derived clusters be used to predict functional modules and protein function? Bioinformatics, 2009. 25(23): p. 3143–50.
Myers, C.L., et al., Finding function: evaluation methods for functional genomic data. BMC Genomics, 2006. 7: p. 187.
Zhu, X., J. Lafferty, and Z. Ghahramani. Semi-supervised learning using Gaussian fields and harmonic functions. in International Conference on Machine Learning. 2003. Washington DC, USA.
Lewis, D.P., T. Jebara, and W.S. Noble, Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure. Bioinformatics, 2006. 22(22): p. 2753–60.
Warde-Farley, D., et al., The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res, 2010. 38 Suppl: p. W214–20.
Alexeyenko, A. and E.L. Sonnhammer, Global networks of functional coupling in eukaryotes from comprehensive data integration. Genome Res, 2009. 19(6): p. 1107–16.
von Mering, C., et al., STRING: known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Res, 2005. 33(Database issue): p. D433–7.
Guan, Y., et al., A genomewide functional network for the laboratory mouse. PLoS Comput Biol, 2008. 4(9): p. e1000165.
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Mostafavi, S., Goldenberg, A., Morris, Q. (2011). Predicting Node Characteristics from Molecular Networks. In: Cagney, G., Emili, A. (eds) Network Biology. Methods in Molecular Biology, vol 781. Humana Press. https://doi.org/10.1007/978-1-61779-276-2_20
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DOI: https://doi.org/10.1007/978-1-61779-276-2_20
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