Protein function prediction using neighbor relativity in protein–protein interaction network

Moosavi, Sobhan; Rahgozar, Maseud; Rahimi, A. M.

doi:10.1016/j.compbiolchem.2012.12.003

Cited by 23 publications

(13 citation statements)

References 19 publications

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“…As technologies in the experiments progress, the network-based methods can be improved and widely extended. For instance, even though the neighborhood counting method [1] in protein function prediction only considers the count of annotated functions in the neighborhood from a PPI network, the recently developed methods [72, 83] can contain not only neighborhood information but also functional similarities from multiple networks. In addition, module-assisted methods that focus on identifying a functional group of proteins are also available and well summarized in [122].…”

Section: Discussionmentioning

confidence: 99%

“…Third, the sequential linear neighborhood propagation method [71] sequentially updates unlabeled proteins according to their shortest path distance to the set of labeled proteins. Finally, the neighbor relativity coefficient (NRC) method [72] derives the NRC score by integrating various graph topological properties, such as the shortest path distance, path connectivity, and common neighbors.…”

Section: Network-based Applicationsmentioning

confidence: 99%

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Review of Biological Network Data and Its Applications

Kim

Xiao

et al. 2013

Genomics Inform

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Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.

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Section: Discussionmentioning

confidence: 99%

Section: Network-based Applicationsmentioning

confidence: 99%

Review of Biological Network Data and Its Applications

Kim

Xiao

et al. 2013

Genomics Inform

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“…We identified four state-of-the-art neighborhood analysis methods and compared their performances for our Saccharomyces cerevisiae dataset with each other and with our methods. We chose the neighborhood counting method of Schwikowski et al [1], the chi-square method of Hishigaki et al [2], a recent version of the neighbor relativity coefficient (NRC) of Moosavi et al [21] and the FS-weight based method of Chua et al [23]. The work of Moosavi et al [21], clearly the strongest of the four methods, focuses on the prediction of three functional groups.…”

Section: Resultsmentioning

confidence: 99%

“…Protein path connectivity score Protein path connectivity score [21] is defined as a measure for network connectivity. It is based on paths between two proteins in an interaction network and is calculated as:…”

Section: Relative Functional Similaritymentioning

confidence: 99%

FunPred-1: Protein function prediction from a protein interaction network using neighborhood analysis

Saha¹,

Chatterjee²,

Basu

et al. 2014

Cellular and Molecular Biology Letters

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Abstract:Proteins are responsible for all biological activities in living organisms. Thanks to genome sequencing projects, large amounts of DNA and protein sequence data are now available, but the biological functions of many proteins are still not annotated in most cases. The unknown function of such non-annotated proteins may be inferred or deduced from their neighbors in a protein interaction network. In this paper, we propose two new methods to predict protein functions based on network neighborhood properties. FunPred 1.1 uses a combination of three simple-yet-effective scoring techniques: the neighborhood ratio, the protein path connectivity and the relative functional similarity. FunPred 1.2 applies a heuristic approach using the edge clustering coefficient to reduce the search space by identifying densely connected neighborhood regions. The overall accuracy achieved in FunPred 1.2 over 8 functional groups involving hetero-interactions in 650 yeast proteins is around 87%, which is higher than the accuracy with FunPred 1.1. It is also higher than the accuracy of many of the state-of-the-art protein function prediction methods described in the literature. The test datasets and the complete source code of the developed software are now freely available at

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“…Some others design probabilistic approaches such as Markov random field (Kui et al, 2002;Letovsky and Kasif, 2003). A more complicated method is to use multiple networks (Deng et al, 2004;Lee et al, 2006) or multiple other data sources, such as genetic interactions and coexpression interactions (Joshi et al, 2004) and sematic similarity between function classes (Moosavi et al, 2013;Jiang and McQuay, 2012), to enrich the information of PPI networks for neighbour-based functional prediction.…”

Section: Introductionmentioning

confidence: 99%

k-Partite cliques of protein interactions: A novel subgraph topology for functional coherence analysis on PPI networks

Liu

Chen

2014

Journal of Theoretical Biology

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Many studies are aimed at identifying dense clusters/subgraphs from protein-protein interaction (PPI) networks for protein function prediction. However, the prediction performance based on the dense clusters is actually worse than a simple guilt-by-association method using neighbor counting ideas. This indicates that the local topological structures and properties of PPI networks are still open to new theoretical investigation and empirical exploration. We introduce a novel topological structure called k-partite cliques of protein interactions-a functionally coherent but not-necessarily dense subgraph topology in PPI networks-to study PPI networks. A k-partite protein clique is a maximal k-partite clique comprising two or more nonoverlapping protein subsets between any two of which full interactions are exhibited. In the detection of PPI's maximal k-partite cliques, we propose to transform PPI networks into induced K-partite graphs where edges exist only between the partites. Then, we present a maximal k-partite clique mining (MaCMik) algorithm to enumerate maximal k-partite cliques from K-partite graphs. Our MaCMik algorithm is then applied to a yeast PPI network. We observed interesting and unusually high functional coherence in k-partite protein cliques-the majority of the proteins in k-partite protein cliques, especially those in the same partites, share the same functions, although k-partite protein cliques are not restricted to be dense compared with dense subgraph patterns or (quasi-)cliques. The idea of k-partite protein cliques provides a novel approach of characterizing PPI networks, and so it will help function prediction for unknown proteins.

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Protein function prediction using neighbor relativity in protein–protein interaction network

Cited by 23 publications

References 19 publications

Review of Biological Network Data and Its Applications

Review of Biological Network Data and Its Applications

FunPred-1: Protein function prediction from a protein interaction network using neighborhood analysis

k-Partite cliques of protein interactions: A novel subgraph topology for functional coherence analysis on PPI networks

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