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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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Journal ArticleDOI
TL;DR: It is illustrated that InWeb_InBioMap enables functional interpretation of >4,700 cancer genomes and genes involved in autism and better functional biological relevance than comparable resources.
Abstract: Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (InWeb_InBioMap, or InWeb_IM) with severalfold more interactions (>500,000) and better functional biological relevance than comparable resources. We illustrate that InWeb_InBioMap enables functional interpretation of >4,700 cancer genomes and genes involved in autism.

468 citations

Journal ArticleDOI
01 Jun 2008-Ecology
TL;DR: The day-to-day dynamics of an arctic pollination interaction network over two consecutive seasons are studied and temporal dynamics provides a mechanistic explanation for previously reported network patterns such as the heterogeneous distribution of number of interactions across species.
Abstract: Despite a strong current interest in ecological networks, the bulk of studies are static descriptions of the structure of networks, and very few analyze their temporal dynamics. Yet, understanding network dynamics is important in order to relate network patterns to ecological processes. We studied the day-to-day dynamics of an arctic pollination interaction network over two consecutive seasons. First, we found that new species entering the network tend to interact with already well-connected species, although there are deviations from this trend due, for example, to morphological mismatching between plant and pollinator traits and nonoverlapping phenophases of plant and pollinator species. Thus, temporal dynamics provides a mechanistic explanation for previously reported network patterns such as the heterogeneous distribution of number of interactions across species. Second, we looked for the ecological properties most likely to be mediating this dynamical process and found that both abundance and phenophase length were important determinants of the number of links per species.

456 citations

Journal ArticleDOI
TL;DR: The in silico inference methods developed for the accurate computational prediction of the interaction of RBP–lncRNA pairs offer essential clues for a better understanding of lncRNA cellular mechanisms and their disease-associated perturbations.
Abstract: Long non-coding RNAs (lncRNAs) are associated to a plethora of cellular functions, most of which require the interaction with one or more RNA-binding proteins (RBPs); similarly, RBPs are often able to bind a large number of different RNAs. The currently available knowledge is already drawing an intricate network of interactions, whose deregulation is frequently associated to pathological states. Several different techniques were developed in the past years to obtain protein–RNA binding data in a high-throughput fashion. In parallel, in silico inference methods were developed for the accurate computational prediction of the interaction of RBP–lncRNA pairs. The field is growing rapidly, and it is foreseeable that in the near future, the protein–lncRNA interaction network will rise, offering essential clues for a better understanding of lncRNA cellular mechanisms and their disease-associated perturbations.

454 citations

Journal ArticleDOI
TL;DR: The proposed algorithm makes it possible to detect clusters of proteins in PPI networks which mostly represent molecular biological functional units and can help to predict the functions of proteins, and they are also useful to understand and explain certain biological processes.
Abstract: After complete sequencing of a number of genomes the focus has now turned to proteomics. Advanced proteomics technologies such as two-hybrid assay, mass spectrometry etc. are producing huge data sets of protein-protein interactions which can be portrayed as networks, and one of the burning issues is to find protein complexes in such networks. The enormous size of protein-protein interaction (PPI) networks warrants development of efficient computational methods for extraction of significant complexes. This paper presents an algorithm for detection of protein complexes in large interaction networks. In a PPI network, a node represents a protein and an edge represents an interaction. The input to the algorithm is the associated matrix of an interaction network and the outputs are protein complexes. The complexes are determined by way of finding clusters, i. e. the densely connected regions in the network. We also show and analyze some protein complexes generated by the proposed algorithm from typical PPI networks of Escherichia coli and Saccharomyces cerevisiae. A comparison between a PPI and a random network is also performed in the context of the proposed algorithm. The proposed algorithm makes it possible to detect clusters of proteins in PPI networks which mostly represent molecular biological functional units. Therefore, protein complexes determined solely based on interaction data can help us to predict the functions of proteins, and they are also useful to understand and explain certain biological processes.

448 citations

Journal ArticleDOI
TL;DR: It is demonstrated that a probabilistic analysis integrating model organism interactome data, protein domain data, genome-wide gene expression data and functional annotation data predicts nearly 40,000 protein-protein interactions in humans—a result comparable to those obtained with experimental and computational approaches in model organisms.
Abstract: A catalog of all human protein-protein interactions would provide scientists with a framework to study protein deregulation in complex diseases such as cancer. Here we demonstrate that a probabilistic analysis integrating model organism interactome data, protein domain data, genome-wide gene expression data and functional annotation data predicts nearly 40,000 protein-protein interactions in humans-a result comparable to those obtained with experimental and computational approaches in model organisms. We validated the accuracy of the predictive model on an independent test set of known interactions and also experimentally confirmed two predicted interactions relevant to human cancer, implicating uncharacterized proteins into definitive pathways. We also applied the human interactome network to cancer genomics data and identified several interaction subnetworks activated in cancer. This integrative analysis provides a comprehensive framework for exploring the human protein interaction network.

446 citations


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