Abstract
Biology has amassed a wealth of information about the function of a multitude of protein-coding genes across species. The challenge now is to understand how all these proteins work together to form a living organism, and a crucial step for gaining this knowledge is a complete description of the molecular “wiring circuits” that underlie cellular processes. In this chapter, we describe a general computational framework for predicting multi-protein assemblies from biochemical co-fractionation data.
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References
Lucas Hu Ming FG, Cuihong Wan, Gary Bader, Andrew Emili (2018) EPIC: elution profile-based inference of protein complex membership. Under revision.
Havugimana PC et al (2012) A census of human soluble protein complexes. Cell 150(5):1068–1081
Wan C et al (2015) Panorama of ancient metazoan macromolecular complexes. Nature 525(7569):339–344
Shannon P et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504
Ruepp A et al (2010) CORUM: the comprehensive resource of mammalian protein complexes—2009. Nucleic Acids Res 38(suppl 1):D497–D501
Kerrien S et al (2012) The IntAct molecular interaction database in 2012. Nucleic Acids Res 40(D1):D841–D846
Gene Ontology C (2015) Gene ontology consortium: going forward. Nucleic Acids Res 43(Database issue):D1049–D1056
Wehrens, R. and M.R. Wehrens, Package ‘wccsom’. 2015
Sánchez-Taltavull D et al (2016) Bayesian correlation analysis for sequence count data. PLoS One 11(10):e0163595
Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Szklarczyk D et al (2017) The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res 45(D1):D362–D368
Warde-Farley D et al (2010) The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 38(suppl_2):W214–W220
Davis J and Goadrich M 2006. The relationship between precision-recall and ROC curves. In Proceedings of the 23rd international conference on Machine learning. ACM
Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29–36
Lee I et al (2011) Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res 21(7):1109–1121
Lee I et al (2010) Predicting genetic modifier loci using functional gene networks. Genome Res 20(8):1143–1153
Kim WK, Krumpelman C, Marcotte EM (2008) Inferring mouse gene functions from genomic-scale data using a combined functional network/classification strategy. Genome Biol 9(1):S5
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Goebels, F., Hu, L., Bader, G., Emili, A. (2018). Automated Computational Inference of Multi-protein Assemblies from Biochemical Co-purification Data. In: Marsh, J. (eds) Protein Complex Assembly. Methods in Molecular Biology, vol 1764. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7759-8_25
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DOI: https://doi.org/10.1007/978-1-4939-7759-8_25
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