Network-based prediction of protein function

By Roded Sharan, Igor Ulitsky, Ron Shamir. 2007

1,086 citations recorded by the source corpus.

Functional annotation of proteins is a fundamental problem in the post‐genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module‐assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Information-theoretic evaluation of predicted ontological annotations (2013, background): “…can be carried out to characterize those sequences, computational function prediction has become increasingly useful for the global characterization of genomes and proteomes as well as for guiding biological experiments via prioritization (Rentzsch and Orengo, 2009; Sharan et al. , 2007).”
Enhancing the Prioritization of Disease-Causing Genes through Tissue Specific Protein Interaction Networks (2012, methodology): “In recent years, PPI networks were shown to be a powerful tool in many fields of molecular biology, such as predicting protein annotation and more [19,49,50].”
In silico prediction of protein-protein interactions in human macrophages (2001, background): “Bioinformatic analyses of these networks have led to numerous functional insights such as function prediction for uncharacterised proteins [13-18], evolution of the function of the duplicated genes [19-21] and the organisation of the signalling pathways [22,23].”
Systems Pharmacology: Network Analysis to Identify Multiscale Mechanisms of Drug Action (2012, background): “However, the interaction networks require the least amount of knowledge regarding nodes and edges, allowing them to be easily constructed and applied to a variety of problems (6, 17–19).”
Multilayer Analysis and Visualization of Networks (2014, background): “We show representative static snapshots of such interactive visualizations in Supplementary Figures 1 B and D and in panel C of Supplementary Figures 2, 4, 6, and 8.”
Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction (2013, methodology): “Using PPI networks in predicting gene function Recent reviews of the latest techniques that use PPI data for protein function prediction [2,37,43,44] suggest that it is possible to distinguish two major approaches.”
Network-based methods for human disease gene prediction. (2011, background): “Hence, a key step is to measure the distance between candidate genes and known disease genes in the PPI network, for which an increasing number of approaches have been developed [33, 34].”
GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function (2008, background): “Though a large number of algorithms have been developed to predict the function of unannotated genes by combining heterogeneous data sources (see [16] for a recent review), the most accurate of these algorithms have long running times, which can range from minutes [17] to hours [9] on yeast.”
A DIseAse MOdule Detection (DIAMOnD) Algorithm Derived from a Systematic Analysis of Connectivity Patterns of Disease Proteins in the Human Interactome (2015, background): “(b) Physically interacting proteins often share functional annotations and pathways [10,25].”
STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data (2015, background): “The role of small molecules in biological systems can be understood only in the relation to the function of the targeted biomolecules, which, in turn, is largely defined by their interaction partners (1–3).”
Learning the Structural Vocabulary of a Network (2017, background): “…of complex networks has become essential for numerous problems in systems biology, including predicting protein interactions and protein function (Sharan, Ulitsky, & Shamir, 2007), identifying robust and fragile modules (Song & Singh, 2013; Navlakha, He, Faloutsos, & Bar-Joseph, 2014), and…”