title: Predicting node characteristics from molecular networks.
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.
authors.affiliation: Department of Computer Science, Centre for Cellular and Biomolecular Research (CCBR), University of Toronto, Toronto, ON, Canada.
authors.forename: Sara
authors.initials: S
authors.lastname: Mostafavi
authors.forename: Anna
authors.initials: A
authors.lastname: Goldenberg
authors.forename: Quaid
authors.initials: Q
authors.lastname: Morris
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D030562:Databases, Protein; D053263:Gene Regulatory Networks; D011237:Predictive Value of Tests; D060066:Protein Interaction Maps / Q000235:genetics; D011506:Proteins / Q000235:genetics / Q000378:metabolism; D012984:Software
chemical_list: D011506:Proteins