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Interaction network

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


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Book ChapterDOI
TL;DR: This chapter describes the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained NaïveBayesian Classifiers, and finally constructing the functional interaction database.
Abstract: Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naive Bayesian Classifier, predicting interactions based on the trained Naive Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.

94 citations

Proceedings ArticleDOI
01 Dec 2013
TL;DR: This paper investigates a bipartite consensus process, in which all the agents converge to a final state characterized by identical modulus but opposite sign, and introduces signless Laplacian matrix and signed Laplacan matrix to analyze the bipartITE consensus of multi-agent systems on homogeneous networks and heterogenous networks, respectively.
Abstract: Collective dynamics is a complex emergence phenomenon yielded by local interactions within multi-agent systems. When agents cooperate or compete in the community, a collective behavior, such as consensus, polarization or diversity, may emerge. In this paper, we investigate a bipartite consensus process, in which all the agents converge to a final state characterized by identical modulus but opposite sign. Firstly, the interaction network of the agents is represented by a directed signed graph. A neighbor-based interaction rule is proposed for each agent with a single integrator dynamics. Then, we classify the signed network into heterogeneous networks and homogeneous networks according to the sign of edges. Under a weak connectivity assumption that the signed network has a spanning tree, some sufficient conditions are derived for bipartite consensus of multi-agent systems with the help of a structural balance theory. At the same time, signless Laplacian matrix and signed Laplacian matrix are introduced to analyze the bipartite consensus of multi-agent systems on homogeneous networks and heterogenous networks, respectively. Finally, simulation results are provided to demonstrate the bipartite consensus formation.

94 citations

Journal ArticleDOI
21 Sep 2012-PLOS ONE
TL;DR: A genome-wide network-based prioritization framework named GUILD, which is able to significantly highlight disease-gene associations that are not used a priori, is proposed and suggested to identify genes implicated in the pathology of human disorders independent of the loci associated with the disorders.
Abstract: Complex genetic disorders often involve products of multiple genes acting cooperatively. Hence, the pathophenotype is the outcome of the perturbations in the underlying pathways, where gene products cooperate through various mechanisms such as protein-protein interactions. Pinpointing the decisive elements of such disease pathways is still challenging. Over the last years, computational approaches exploiting interaction network topology have been successfully applied to prioritize individual genes involved in diseases. Although linkage intervals provide a list of disease-gene candidates, recent genome-wide studies demonstrate that genes not associated with any known linkage interval may also contribute to the disease phenotype. Network based prioritization methods help highlighting such associations. Still, there is a need for robust methods that capture the interplay among disease-associated genes mediated by the topology of the network. Here, we propose a genome-wide network-based prioritization framework named GUILD. This framework implements four network-based disease-gene prioritization algorithms. We analyze the performance of these algorithms in dozens of disease phenotypes. The algorithms in GUILD are compared to state-of-the-art network topology based algorithms for prioritization of genes. As a proof of principle, we investigate top-ranking genes in Alzheimer's disease (AD), diabetes and AIDS using disease-gene associations from various sources. We show that GUILD is able to significantly highlight disease-gene associations that are not used a priori. Our findings suggest that GUILD helps to identify genes implicated in the pathology of human disorders independent of the loci associated with the disorders.

94 citations

Journal ArticleDOI
TL;DR: Results show that the reverse engineering scheme to discover genetic regulation from genome-wide transcription data that monitors the dynamic transcriptional response after a change in cellular environment discovers genetic interactions that display significant enrichment of co-citation in literature.
Abstract: Motivation: We propose a reverse engineering scheme to discover genetic regulation from genome-wide transcription data that monitors the dynamic transcriptional response after a change in cellular environment. The interaction network is estimated by solving a linear model using simultaneous shrinking of the least absolute weights and the prediction error. Results: The proposed scheme has been applied to the murine C2C12 cell-line stimulated to undergo osteoblast differentiation. Results show that our method discovers genetic interactions that display significant enrichment of co-citation in literature. More detailed study showed that the inferred network exhibits properties and hypotheses that are consistent with current biological knowledge. Availability: Software is freely available for academic use as a Matlab package called GENLAB: http://genlab.tudelft.nl/genlab.html Contact: E.P.vanSomeren@tudelft.nl Supplementary information: Additional data, results and figures can be found at http://genlab.tudelft.nl/larna.html

93 citations

Journal ArticleDOI
TL;DR: This work presents the first model where a network of eukaryotic TFs has evolved via rounds of WGD, and provides evidence that an initial network, formed by 9-11 homodimerizing proteins interacting with each other, existed in the common ancestor of all seed plants.
Abstract: Recent investigations on metazoan transcription factors (TFs) indicate that single-gene duplication events and the gain and loss of protein domains are 2 crucial factors in shaping their protein-protein interaction networks. Plant genomes, on the other hand, have a history of polyploidy and whole-genome duplications (WGDs), and thus, their study helps to understand whether WGDs have also had a significant influence on protein network evolution. Here we investigate the evolution of the interaction network in the well-studied MADS domain MIKC-type proteins, a TF family which plays an important role in both the vegetative and the reproductive phases of plant life. We combine phylogenetic reconstruction, protein domain analysis, and interaction data from different species. We show that, unlike previously analyzed interaction networks, the MIKC-type protein network displays a characteristic topology, with overall high inter-subfamily connectivity, shared interactors between paralogs, and conservation of interaction patterns across species. The evaluation of the number of MIKC-type proteins at key time points throughout the evolution of land plants in the lineage leading to Arabidopsis suggested that most duplicates were retained after each round of WGD. We provide evidence that an initial network, formed by 9-11 homodimerizing proteins interacting with each other, existed in the common ancestor of all seed plants. This basic structure has been conserved after each round of WGD, adding layers of paralogs with similar interaction patterns. We thus present the first model where we can show that a network of eukaryotic TFs has evolved via rounds of WGD. Furthermore, we found that in subfamilies in which the K domain is most diverged, the interactions with other subfamilies have been largely lost. We discuss the possibility that such a high proportion of genes were retained after each WGD because of their capacity to form higher order complexes involving proteins from different subfamilies. The simultaneous duplications allowed for the conservation of the quantitative balance between the constituents and facilitated sub- and neofunctionalization through differential expression of whole units.

93 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202337
202290
2021183
2020221
2019201
2018163