Interaction network

About: Interaction network is a research topic. Over the lifetime, 2700 publications have been published within this topic receiving 113372 citations.

Detecting protein complexes in protein interaction networks using a ranking algorithm with a refined merging procedure.

Abstract: Developing suitable methods for the identification of protein complexes remains an active research area. It is important since it allows better understanding of cellular functions as well as malfunctions and it consequently leads to producing more effective cures for diseases. In this context, various computational approaches were introduced to complement high-throughput experimental methods which typically involve large datasets, are expensive in terms of time and cost, and are usually subject to spurious interactions. In this paper, we propose ProRank+, a method which detects protein complexes in protein interaction networks. The presented approach is mainly based on a ranking algorithm which sorts proteins according to their importance in the interaction network, and a merging procedure which refines the detected complexes in terms of their protein members. ProRank + was compared to several state-of-the-art approaches in order to show its effectiveness. It was able to detect more protein complexes with higher quality scores. The experimental results achieved by ProRank + show its ability to detect protein complexes in protein interaction networks. Eventually, the method could potentially identify previously-undiscovered protein complexes. The datasets and source codes are freely available for academic purposes at http://faculty.uaeu.ac.ae/nzaki/Research.htm .

A visual-analytic toolkit for dynamic interaction graphs

Abstract: In this article we describe a visual-analytic tool for the interrogation of evolving interaction network data such as those found in social, bibliometric, WWW and biological applications. The tool we have developed incorporates common visualization paradigms such as zooming, coarsening and filtering while naturally integrating information extracted by a previously described event-driven framework for characterizing the evolution of such networks. The visual front-end provides features that are specifically useful in the analysis of interaction networks, capturing the dynamic nature of both individual entities as well as interactions among them. The tool provides the user with the option of selecting multiple views, designed to capture different aspects of the evolving graph from the perspective of a node, a community or a subset of nodes of interest. Standard visual templates and cues are used to highlight critical changes that have occurred during the evolution of the network. A key challenge we address in this work is that of scalability - handling large graphs both in terms of the efficiency of the back-end, and in terms of the efficiency of the visual layout and rendering. Two case studies based on bibliometric and Wikipedia data are presented to demonstrate the utility of the toolkit for visual knowledge discovery.

DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network

Abstract: Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network's adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The python package can be obtained from git.io/diffslcpy. The R implementation and code to reproduce the analysis is available via git.io/diffslc.

Rice mitogen activated protein kinase kinase and mitogen activated protein kinase interaction network revealed by in-silico docking and yeast two-hybrid approaches.

Abstract: Protein-protein interaction is one of the crucial ways to decipher the functions of proteins and to understand their role in complex pathways at cellular level. Such a protein-protein interaction network in many crop plants remains poorly defined owing largely to the involvement of high costs, requirement for state of the art laboratory, time and labour intensive techniques. Here, we employed computational docking using ZDOCK and RDOCK programmes to identify interaction network between members of Oryza sativa mitogen activated protein kinase kinase (MAPKK) and mitogen activated protein kinase (MAPK). The 3-dimentional (3-D) structures of five MAPKKs and eleven MAPKs were determined by homology modelling and were further used as input for docking studies. With the help of the results obtained from ZDOCK and RDOCK programmes, top six possible interacting MAPK proteins were predicted for each MAPKK. In order to assess the reliability of the computational prediction, yeast two-hybrid (Y2H) analyses were performed using rice MAPKKs and MAPKs. A direct comparison of Y2H assay and computational prediction of protein interaction was made. With the exception of one, all the other MAPKK-MAPK pairs identified by Y2H screens were among the top predictions by computational dockings. Although, not all the predicted interacting partners could show interaction in Y2H, yet, the harmony between the two approaches suggests that the computational predictions in the present work are reliable. Moreover, the present Y2H analyses per se provide interaction network among MAPKKs and MAPKs which would shed more light on MAPK signalling network in rice.

Interaction Networks: Generating High Level Hints Based on Network Community Clusterings

Abstract: We introduce a novel data structure, the Interaction Network, for representing interaction-data from open problem solving environment tutors We show how using network community detecting techniques are used to identify subgoals in problems in a logic tutor We then use those community structures to generate high level hints between subgoals The preliminary results show that using network analysis techniques are promising for exploring and understanding user data from open problem solving environments