Network‐based prediction of protein function

Abstract

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.

A neural network algorithm for semi-supervised node label learning from unbalanced data

“…By extending this approach, indirect neighbours, that account for pairs of nodes connected through intermediate ones, have been used to extend the notion of pairwise-similarities among nodes (Chua et al, 2006;Li et al, 2010;Bogdanov & Singh, 2010). Other methods focused on clustering nodes into functional modules based on the graph topology, and assigning to unlabeled nodes the most common labels in a given module (Sharan et al, 2007;Zhu et al, 2010). Furthermore, nodes can propagate labels to their neighbors with an iterative process until convergence (Zhu et al, 2003;Zhou et al, 2004).…”

Protein Interactome and Its Application to Protein Function Prediction

“…In the post-genomic era, functional genomics is an emerging area of research that seeks to annotate every bit of information of the genome structure with relevant biological function. Still, many proteins (or genes) remain functionally unannotated (Apweiler et al, 2004;Sharan et al, 2007). These missing links between structures and functions need to be resolved to understand complex biological phenomena including human diseases, development and aging.…”

Basic networks: Definition and applications

“…Among the most significant examples are protein-protein interaction networks, networks based in cooccurrence of protein domains or graphs generated by assigning edges to genes with a given level of coexpression (Aittokallio and Schwikowski 2006;Sharan et al 2007). All these graphs have emerged very recently, with the advent of genomic and proteomic high-throughput technologies that have generated huge amounts of data.…”

“…The problem then consists in defining the best way to determine the connectors. In the current literature, this has been generally solved by devising strategies to define modules (e. g. Del Rio et al 2001;Bader 2003;Ashtana et al 2004;Hashimoto et al 2004;Krauthamer et al 2004;Arnau et al 2005;Scott et al 2005;Lubovac et al 2006;Lucas et al 2006;Li and Horvath 2007; see review by Sharan et al 2007) A module is loosely defined as a group of closely linked nodes, with an internal cohesion that allows its separation from the rest of units in the network. The problem is that the characterization of modules is based on conventional, a priori criteria of unknown efficiency.…”