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Showing papers on "Interaction network published in 2017"


Journal ArticleDOI
TL;DR: To facilitate network-based approaches to drug discovery, BioGRID now incorporates 27 501 chemical–protein interactions for human drug targets, as drawn from the DrugBank database.
Abstract: The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the annotation and archival of protein, genetic and chemical interactions for all major model organism species and humans. As of September 2016 (build 3.4.140), the BioGRID contains 1 072 173 genetic and protein interactions, and 38 559 post-translational modifications, as manually annotated from 48 114 publications. This dataset represents interaction records for 66 model organisms and represents a 30% increase compared to the previous 2015 BioGRID update. BioGRID curates the biomedical literature for major model organism species, including humans, with a recent emphasis on central biological processes and specific human diseases. To facilitate network-based approaches to drug discovery, BioGRID now incorporates 27 501 chemical-protein interactions for human drug targets, as drawn from the DrugBank database. A new dynamic interaction network viewer allows the easy navigation and filtering of all genetic and protein interaction data, as well as for bioactive compounds and their established targets. BioGRID data are directly downloadable without restriction in a variety of standardized formats and are freely distributed through partner model organism databases and meta-databases.

892 citations


Journal ArticleDOI
TL;DR: OhmNet, a hierarchy‐aware unsupervised node feature learning approach for multi‐layer networks, is presented and it is demonstrated that it is possible to leverage the tissue hierarchy in order to effectively transfer cellular functions to a functionally uncharacterized tissue.
Abstract: Motivation Understanding functions of proteins in specific human tissues is essential for insights into disease diagnostics and therapeutics, yet prediction of tissue-specific cellular function remains a critical challenge for biomedicine. Results Here, we present OhmNet , a hierarchy-aware unsupervised node feature learning approach for multi-layer networks. We build a multi-layer network, where each layer represents molecular interactions in a different human tissue. OhmNet then automatically learns a mapping of proteins, represented as nodes, to a neural embedding-based low-dimensional space of features. OhmNet encourages sharing of similar features among proteins with similar network neighborhoods and among proteins activated in similar tissues. The algorithm generalizes prior work, which generally ignores relationships between tissues, by modeling tissue organization with a rich multiscale tissue hierarchy. We use OhmNet to study multicellular function in a multi-layer protein interaction network of 107 human tissues. In 48 tissues with known tissue-specific cellular functions, OhmNet provides more accurate predictions of cellular function than alternative approaches, and also generates more accurate hypotheses about tissue-specific protein actions. We show that taking into account the tissue hierarchy leads to improved predictive power. Remarkably, we also demonstrate that it is possible to leverage the tissue hierarchy in order to effectively transfer cellular functions to a functionally uncharacterized tissue. Overall, OhmNet moves from flat networks to multiscale models able to predict a range of phenotypes spanning cellular subsystems. Availability and implementation Source code and datasets are available at http://snap.stanford.edu/ohmnet . Contact jure@cs.stanford.edu.

472 citations


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
TL;DR: This work presents research highlights ranging from determination of the molecular interaction network within a cell to studies of architectural and functional properties of brain networks and biological transportation networks, and focuses on synergies between network science and data analysis, which enable us to determine functional connectivity patterns in multicellular systems.

306 citations


Journal ArticleDOI
TL;DR: A satisfactory resolution of the underlying factors determining network structure will require substantial additional information, not only on independently assessed abundances, but also on traits, and ideally on fitness cons...
Abstract: Ecological networks depict the interactions between species, mainly based on observations in the field. The information contained in such interaction matrices depends on the sampling design, and typically, compounds preferences (specialization) and abundances (activity). Null models are the primary vehicles to disentangle the effects of specialization from those of sampling and abundance, but they ignore the feedback of network structure on abundances. Hence, network structure, as exemplified here by modularity, is difficult to link to specific causes. Indeed, various processes lead to modularity and to specific interaction patterns more generally. Inferring (co)evolutionary dynamics is even more challenging, as competition and trait matching yield identical patterns of interactions. A satisfactory resolution of the underlying factors determining network structure will require substantial additional information, not only on independently assessed abundances, but also on traits, and ideally on fitness cons...

153 citations


Journal ArticleDOI
TL;DR: The authors take an in silico naïve Bayesian classifier approach to integrate multiple lines of evidence for E3-substrate prediction, enabling prediction of the proteome-wide human E3 ligase interaction network.
Abstract: The ubiquitination mediated by ubiquitin activating enzyme (E1), ubiquitin conjugating enzyme (E2), and ubiquitin ligase (E3) cascade is crucial to protein degradation, transcription regulation, and cell signaling in eukaryotic cells. The high specificity of ubiquitination is regulated by the interaction between E3 ubiquitin ligases and their target substrates. Unfortunately, the landscape of human E3-substrate network has not been systematically uncovered. Therefore, there is an urgent need to develop a high-throughput and efficient strategy to identify the E3-substrate interaction. To address this challenge, we develop a computational model based on multiple types of heterogeneous biological evidence to investigate the human E3-substrate interactions. Furthermore, we provide UbiBrowser as an integrated bioinformatics platform to predict and present the proteome-wide human E3-substrate interaction network ( http://ubibrowser.ncpsb.org ).

111 citations


Journal ArticleDOI
TL;DR: The review analysis concluded that, while Protein- Protein Interaction was used to be characterized just by their large and plain interacting surfaces, they were considered inapplicable for drug discovery studies for a long time.
Abstract: Background The investigation of the cellular components, their interactions and related functions constitute the major conditions in order to understand the cell as an integrated system. More specifically, the Protein-Protein Interactions and the obtained networks are very important in the majority of biological functions and processes, while most of the proteins appear to activate their functionalities through their interaction. Methods Our in depth review analysis, include Sixty-five peer-reviewed research and review studies from several bibliographic databases. The most significant components were fully described, filtered, combined and analyzed in order to provide documented proofs on the Protein-Protein Interaction Network' applications in biomedicine. Results The Protein-Protein Interaction Network' alignment and mapping give the opportunity of further knowledge extraction concerning the evolutionary relationships between the species through conserved pathways and protein complexes. Additionally, Protein-Protein Interaction Network information has been demonstrated to be able to predict functionally orthologous proteins within sequence homology clusters. Our review analysis concluded that, while Protein- Protein Interaction was used to be characterized just by their large and plain interacting surfaces, they were considered inapplicable for drug discovery studies for a long time. Conclusion The present review explores multiple technologies implicated in Protein-Protein Interaction Networks, implicating their potential role in drug discovery mechanisms.

101 citations


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


Journal ArticleDOI
TL;DR: The proposed PLPIHS, an effective computational method for Predicting lncRNA-Protein Interactions using HeteSim Scores, performs significantly better than the existing state-of-the-art approaches and achieves an AUC score of 0.97 in the leave-one-out validation test.
Abstract: Massive studies have indicated that long non-coding RNAs (lncRNAs) are critical for the regulation of cellular biological processes by binding with RNA-related proteins. However, only a few experimentally supported lncRNA-protein associations have been reported. Existing network-based methods are typically focused on intrinsic features of lncRNA and protein but ignore the information implicit in the topologies of biological networks associated with lncRNAs. Considering the limitations in previous methods, we propose PLPIHS, an effective computational method for Predicting lncRNA-Protein Interactions using HeteSim Scores. PLPIHS uses the HeteSim measure to calculate the relatedness score for each lncRNA-protein pair in the heterogeneous network, which consists of lncRNA-lncRNA similarity network, lncRNA-protein association network and protein-protein interaction network. An SVM classifier to predict lncRNA-protein interactions is built with the HeteSim scores. The results show that PLPIHS performs significantly better than the existing state-of-the-art approaches and achieves an AUC score of 0.97 in the leave-one-out validation test. We also compare the performances of networks with different connectivity density and find that PLPIHS performs well across all the networks. Furthermore, we use the proposed method to identify the related proteins for lncRNA MALAT1. Highly-ranked proteins are verified by the biological studies and demonstrate the effectiveness of our method.

76 citations


Journal ArticleDOI
TL;DR: A summary of methods for building proteome-scale interactome maps and the current status and implications of mapping achievements are discussed.

60 citations


Proceedings ArticleDOI
02 Feb 2017
TL;DR: This work shows that it is indeed possible to achieve the goal of representing the edge structure of the network purely as a function of time with the use of a matrix factorization, in which the entries are parameterized by time.
Abstract: The problem of evolutionary network analysis has gained increasing attention in recent years, because of an increasing number of networks, which are encountered in temporal settings. For example, social networks, communication networks, and information networks continuously evolve over time, and it is desirable to learn interesting trends about how the network structure evolves over time, and in terms of other interesting trends. One challenging aspect of networks is that they are inherently resistant to parametric modeling, which allows us to truly express the edges in the network as functions of time. This is because, unlike multidimensional data, the edges in the network reflect interactions among nodes, and it is difficult to independently model the edge as a function of time, without taking into account its correlations and interactions with neighboring edges. Fortunately, we show that it is indeed possible to achieve this goal with the use of a matrix factorization, in which the entries are parameterized by time. This approach allows us to represent the edge structure of the network purely as a function of time, and predict the evolution of the network over time. This opens the possibility of using the approach for a wide variety of temporal network analysis problems, such as predicting future trends in structures, predicting links, and node-centric anomaly/event detection. This flexibility is because of the general way in which the approach allows us to express the structure of the network as a function of time. We present a number of experimental results on a number of temporal data sets showing the effectiveness of the approach.

Journal ArticleDOI
TL;DR: A novel and efficient approach for the (targeted) structural controllability of cancer networks and a better understanding of the control dynamics of cancer through computational modelling can pave the way for new efficient therapeutic approaches and personalized medicine.
Abstract: Control theory is a well-established approach in network science, with applications in bio-medicine and cancer research. We build on recent results for structural controllability of directed networks, which identifies a set of driver nodes able to control an a-priori defined part of the network. We develop a novel and efficient approach for the (targeted) structural controllability of cancer networks and demonstrate it for the analysis of breast, pancreatic, and ovarian cancer. We build in each case a protein-protein interaction network and focus on the survivability-essential proteins specific to each cancer type. We show that these essential proteins are efficiently controllable from a relatively small computable set of driver nodes. Moreover, we adjust the method to find the driver nodes among FDA-approved drug-target nodes. We find that, while many of the drugs acting on the driver nodes are part of known cancer therapies, some of them are not used for the cancer types analyzed here; some drug-target driver nodes identified by our algorithms are not known to be used in any cancer therapy. Overall we show that a better understanding of the control dynamics of cancer through computational modelling can pave the way for new efficient therapeutic approaches and personalized medicine.

Journal ArticleDOI
TL;DR: A new method is developed that treats the observed network as a sample of the true network with different sampling rates for positive (true edges) and negative (absent edges) examples and obtains a relative ranking of potential links by their probabilities, using information on network topology as well as node covariates if available.
Abstract: Link prediction is one of the fundamental problems in network analysis. In many applications, notably in genetics, a partially observed network may not contain any negative examples, that is, edges known for certain to be absent, which creates a difficulty for existing supervised learning approaches. We develop a new method that treats the observed network as a sample of the true network with different sampling rates for positive (true edges) and negative (absent edges) examples. We obtain a relative ranking of potential links by their probabilities, using information on network topology as well as node covariates if available. The method relies on the intuitive assumption that if two pairs of nodes are similar, the probabilities of these pairs forming an edge are also similar. Empirically, the method performs well under many settings, including when the observed network is sparse. We apply the method to a protein–protein interaction network and a school friendship network.

Journal ArticleDOI
03 Mar 2017
TL;DR: A group of scientists from Denmark and Germany presents the first attempt for a quantitative evaluation of de novo methods, which will allow the bioinformatics community to objectively compare future tools against the state of the art.
Abstract: De novo pathway enrichment is a powerful approach to discover previously uncharacterized molecular mechanisms in addition to already known pathways. To achieve this, condition-specific functional modules are extracted from large interaction networks. Here, we give an overview of the state of the art and present the first framework for assessing the performance of existing methods. We identified 19 tools and selected seven representative candidates for a comparative analysis with more than 12,000 runs, spanning different biological networks, molecular profiles, and parameters. Our results show that none of the methods consistently outperforms the others. To mitigate this issue for biomedical researchers, we provide guidelines to choose the appropriate tool for a given dataset. Moreover, our framework is the first attempt for a quantitative evaluation of de novo methods, which will allow the bioinformatics community to objectively compare future tools against the state of the art. De novo pathway enrichment methods are essential to understand disease complexity. They can uncover disease-specific functional modules by integrating molecular interaction networks with expression profiles. However, how should researchers choose one method out of several? In this article, a group of scientists from Denmark and Germany presents the first attempt to quantitatively evaluate existing methods. This framework will help the biomedical community to find the appropriate tool(s) for their data. They created synthetic gold standards and simulated expression profiles to perform a systematic assessment of various tools. They observed that the choice of interaction network, parameter settings, preprocessing of expression data and statistical properties of the expression profiles influence the results to a large extent. The results reveal strengths and limitations of the individual methods and suggest using two or more tools to obtain comprehensive disease-modules.

Journal ArticleDOI
TL;DR: This multi-layer interaction graph provides a practical framework for the prediction of outage propagation and decision making on mitigation actions and provides useful insights on the mechanism and mitigation of cascading outages, which cannot be obtained from any single-layer.
Abstract: This paper proposes a multi-layer interaction graph on cascading outages of power systems as an extension of a single-layer interaction network proposed previously This multi-layer interaction graph provides a practical framework for the prediction of outage propagation and decision making on mitigation actions It has multiple layers to, respectively, identify key intra-layer links and components within each layer and key inter-layer links and components between layers, which contribute the most to outage propagation Each layer focuses on one of several aspects that are critical for system operators’ decision support, such as the number of line outages, the amount of load shedding, and the electrical distance of outage propagation Besides, the proposed integrated mitigation strategies can limit the propagation of cascading outages by weakening key intra-layer links All layers are constructed offline from a database of simulated cascades and then used online A three-layer interaction graph is presented in detail and demonstrated on the Northeastern Power Coordinating Council 48-machine 140-bus system The key intra- and inter-layer links and key components revealed by the multi-layer interaction graph provide useful insights on the mechanism and mitigation of cascading outages, which cannot be obtained from any single-layer

Journal ArticleDOI
TL;DR: A new method named ThrRW is proposed, which takes several steps of random walking on three different biological networks: protein interaction network (PIN), domain co-occurrence network (DCN), and functional interrelationship network (FIN), respectively, so as to infer functional information from neighbors in the corresponding networks.
Abstract: With the gap between the sequence data and their functional annotations becomes increasing wider, many computational methods have been proposed to annotate functions for unknown proteins. However, designing effective methods to make good use of various biological resources is still a big challenge for researchers due to function diversity of proteins. In this work, we propose a new method named ThrRW, which takes several steps of random walking on three different biological networks: protein interaction network (PIN), domain co-occurrence network (DCN), and functional interrelationship network (FIN), respectively, so as to infer functional information from neighbors in the corresponding networks. With respect to the topological and structural differences of the three networks, the number of walking steps in the three networks will be different. In the course of working, the functional information will be transferred from one network to another according to the associations between the nodes in different networks. The results of experiment on S. cerevisiae data show that our method achieves better prediction performance not only than the methods that consider both PIN data and GO term similarities, but also than the methods using both PIN data and protein domain information, which verifies the effectiveness of our method on integrating multiple biological data sources.

Journal ArticleDOI
TL;DR: An interventional bipartite consensus problem is considered for a high-order multi-agent system with unknown disturbance dynamics and a dynamic output-feedback consensus control is designed for each agent in a fully distributed fashion.
Abstract: In this paper, an interventional bipartite consensus problem is considered for a high-order multi-agent system with unknown disturbance dynamics. The interactions among the agents are cooperative and competitive simultaneously and thus the interaction network (just called coopetition network in sequel for simplicity) is conveniently modeled by a signed graph. When the coopetition network is structurally balanced, all the agents are split into two competitive subgroups. An exogenous system (called leader for simplicity) is introduced to intervene the two competitive subgroups such that they can reach a bipartite consensus. The unknown disturbance dynamics are assumed to have linear parametric models. With the help of the notation of a disagreement state variable, decentralized adaptive laws are proposed to estimate the unknown disturbances and a dynamic output-feedback consensus control is designed for each agent in a fully distributed fashion, respectively. The controller design guarantees that the state matrix of the closed-loop system can be an arbitrary predefined Hurwitz matrix. Under the assumption that the coopetition network is structurally balanced and the leader is a root of the spanning tree in an augmented graph, the bipartite consensus and the parameter estimation are analyzed by invoking a common Lyapunov function method when the coopetition network is time-varying according to a piecewise constant switching signal. Finally, simulation results are given to demonstrate the effectiveness of the proposed control strategy.

Journal ArticleDOI
10 Feb 2017
TL;DR: A meta-analysis of host’s whole blood transcriptomic profiles that were integrated into a genome-scale protein–protein interaction network to generate response networks in active tuberculosis, and reports the emergence of a highly active common core in disease, showing partial reversals upon treatment.
Abstract: Tuberculosis remains a major global health challenge worldwide, causing more than a million deaths annually. To determine newer methods for detecting and combating the disease, it is necessary to characterise global host responses to infection. Several high throughput omics studies have provided a rich resource including a list of several genes differentially regulated in tuberculosis. An integrated analysis of these studies is necessary to identify a unified response to the infection. Such data integration is met with several challenges owing to platform dependency, patient heterogeneity, and variability in the extent of infection, resulting in little overlap among different datasets. Network-based approaches offer newer alternatives to integrate and compare diverse data. In this study, we describe a meta-analysis of host’s whole blood transcriptomic profiles that were integrated into a genome-scale protein–protein interaction network to generate response networks in active tuberculosis, and monitor their behaviour over treatment. We report the emergence of a highly active common core in disease, showing partial reversals upon treatment. The core comprises 380 genes in which STAT1, phospholipid scramblase 1 (PLSCR1), C1QB, OAS1, GBP2 and PSMB9 are prominent hubs. This network captures the interplay between several biological processes including pro-inflammatory responses, apoptosis, complement signalling, cytoskeletal rearrangement, and enhanced cytokine and chemokine signalling. The common core is specific to tuberculosis, and was validated on an independent dataset from an Indian cohort. A network-based approach thus enables the identification of common regulators that characterise the molecular response to infection, providing a platform-independent foundation to leverage maximum insights from available clinical data. Patients suffering from tuberculosis (TB) show a high extent of variations in their gene expression patterns. Such heterogeneity poses major road blocks to our understanding of how hosts respond to the disease. A number of studies have profiled transcriptomes of human blood samples from TB patients, but a meta-analysis indicates that very few changes are consistently seen. The problem, to a large extent, lies with the way large data is analysed. We have used a genome-wide network approach to characterise the host response and have identified a common-core in the TB response networks of different patients, indicating the presence of unified host response mechanisms. This core network provides a comprehensive view into the most significant regulators of the infection-mediated biological processes across patients from different populations, and it shows partial reversals upon treatment.

Book ChapterDOI
TL;DR: In this article, the emergence of global patterns in large groups in first and second-order multiagent systems is studied, focusing on two ingredients that influence the dynamics: the interaction network and the state space.
Abstract: In the present chapter, we study the emergence of global patterns in large groups in first- and second-order multiagent systems, focusing on two ingredients that influence the dynamics: the interaction network and the state space. The state space determines the types of equilibrium that can be reached by the system. Meanwhile, convergence to specific equilibria depends on the connectivity of the interaction network and on the interaction potential. When the system does not satisfy the necessary conditions for convergence to the desired equilibrium, control can be exerted, both on finite-dimensional systems and on their mean-field limit.

Journal ArticleDOI
TL;DR: These findings illustrate that protein interaction evolution occurs at the level of conformational dynamics, when the binding mechanism concerns an induced fit or conformational selection, and can evolve towards increased specificity with reduced flexibility when the complexity of the protein interaction network requires specificity.
Abstract: A key question regarding protein evolution is how proteins adapt to the dynamic environment in which they function and how in turn their evolution shapes the protein interaction network. We used extant and resurrected ancestral plant MADS-domain transcription factors to understand how SEPALLATA3, a protein with hub and glue properties, evolved and takes part in network organization. Although the density of dimeric interactions was saturated in the network, many new interactions became mediated by SEPALLATA3 after a whole genome triplication event. By swapping SEPALLATA3 and its ancestors between dimeric networks of different ages, we found that the protein lost the capacity of promiscuous interaction and acquired specificity in evolution. This was accompanied with constraints on conformations through proline residue accumulation, which made the protein less flexible. SHORT VEGETATIVE PHASE on the other hand (non-hub) was able to gain protein-protein interactions due to a C-terminal domain insertion, allowing for a larger interaction interface. These findings illustrate that protein interaction evolution occurs at the level of conformational dynamics, when the binding mechanism concerns an induced fit or conformational selection. Proteins can evolve towards increased specificity with reduced flexibility when the complexity of the protein interaction network requires specificity.

Journal ArticleDOI
TL;DR: A DNA‐barcode‐based multiplexed protein interaction assay in Saccharomyces cerevisiae is used to measure in vivo abundance of binary protein complexes under 14 environments and the value of this resource is illustrated in revealing mechanisms of network dynamics.
Abstract: Many cellular functions are mediated by protein–protein interaction networks, which are environment dependent. However, systematic measurement of interactions in diverse environments is required to better understand the relative importance of different mechanisms underlying network dynamics. To investigate environment‐dependent protein complex dynamics, we used a DNA‐barcode‐based multiplexed protein interaction assay in Saccharomyces cerevisiae to measure in vivo abundance of 1,379 binary protein complexes under 14 environments. Many binary complexes (55%) were environment dependent, especially those involving transmembrane transporters. We observed many concerted changes around highly connected proteins, and overall network dynamics suggested that “concerted” protein‐centered changes are prevalent. Under a diauxic shift in carbon source from glucose to ethanol, a mass‐action‐based model using relative mRNA levels explained an estimated 47% of the observed variance in binary complex abundance and predicted the direction of concerted binary complex changes with 88% accuracy. Thus, we provide a resource of yeast protein interaction measurements across diverse environments and illustrate the value of this resource in revealing mechanisms of network dynamics.

Journal ArticleDOI
TL;DR: The approach presented in this study can be applied to other complex traits for which risk-causative genes are known as it provides a promising tool for setting the foundations for collating genomics and wet laboratory data in a bidirectional manner and will be critical to accelerate molecular target prioritization and drug discovery.
Abstract: The genetic analysis of complex disorders has undoubtedly led to the identification of a wealth of associations between genes and specific traits. However, moving from genetics to biochemistry one gene at a time has, to date, rather proved inefficient and under-powered to comprehensively explain the molecular basis of phenotypes. Here we present a novel approach, weighted protein–protein interaction network analysis (W-PPI-NA), to highlight key functional players within relevant biological processes associated with a given trait. This is exemplified in the current study by applying W-PPI-NA to frontotemporal dementia (FTD): We first built the state of the art FTD protein network (FTD-PN) and then analyzed both its topological and functional features. The FTD-PN resulted from the sum of the individual interactomes built around FTD-spectrum genes, leading to a total of 4198 nodes. Twenty nine of 4198 nodes, called inter-interactome hubs (IIHs), represented those interactors able to bridge over 60% of the in...

Journal ArticleDOI
TL;DR: This article uses the logistic normal distribution to model the background mechanism of microbiome data, which can appropriately deal with the compositional nature of the data, and proposes a novel penalized maximum likelihood method called gCoda to estimate the sparse structure of inverse covariance for latent normal variables to address the high dimensionality of the microbiome data.
Abstract: The increasing quality and the reducing cost of high-throughput sequencing technologies for 16S rRNA gene profiling enable researchers to directly analyze microbe communities in natural environments. The direct interactions among microbial species of a given ecological system can help us understand the principles of community assembly and maintenance under various conditions. Compositionality and dimensionality of microbiome data are two main challenges for inferring the direct interaction network of microbes. In this article, we use the logistic normal distribution to model the background mechanism of microbiome data, which can appropriately deal with the compositional nature of the data. The direct interaction relationships are then modeled via the conditional dependence network under this logistic normal assumption. We then propose a novel penalized maximum likelihood method called gCoda to estimate the sparse structure of inverse covariance for latent normal variables to address the high dimensionality of the microbiome data. An effective Majorization-Minimization algorithm is proposed to solve the optimization problem in gCoda. Simulation studies show that gCoda outperforms existing methods (e.g., SPIEC-EASI) in edge recovery of inverse covariance for compositional data under a variety of scenarios. gCoda also performs better than SPIEC-EASI for inferring direct microbial interactions of mouse skin microbiome data.

Journal ArticleDOI
TL;DR: The design and implementation of an integrated approach aiming to unravel the complexity of the interaction network based on Storytelling, the Problem Structuring Method, and Social Network Analysis are detailed.
Abstract: There is growing awareness that fast response to emergency situation requires effective coordination among several institutional and non-institutional actors. The most common approaches, based on innovating technologies for information collection and management, are not sufficient to cope with the increasing complexity of emergency management. This work demonstrates that effective cooperation claims for a shift from information management to interaction management. Therefore, methods and tools are required in order to better understand the complexity of the interactions taking place during an emergency, and to analyse the actual roles and responsibilities of the different actors. This paper details the design and implementation of an integrated approach aiming to unravel the complexity of the interaction network based on Storytelling, the Problem Structuring Method, and Social Network Analysis. The potential of the integrated approach has been investigated in the Lorca (Spain) flood risk management case study.

Posted ContentDOI
16 Aug 2017-bioRxiv
TL;DR: The network of pairwise competitive interactions in a model community consisting of 20 strains of naturally co-occurring soil bacteria is investigated and it is found that the interaction network is strongly hierarchical and lacks significant non-transitive motifs, a result that is robust across multiple environments.
Abstract: Microbial communities are typically incredibly diverse, and this diversity is thought to play a key role in community function. However, explaining how this diversity can be maintained is a major challenge in ecology. Temporal fluctuations and spatial structure in the environment likely play a key role, but it has also been suggested that the structure of interactions within the community may act as a stabilizing force for species diversity. In particular, if competitive interactions are non-transitive as in the classic rock-paper-scissors game, they can contribute to the maintenance of species diversity; on the other hand, if they are predominantly hierarchical, any observed diversity must be maintained via other mechanisms. Here, we investigate the network of pairwise competitive interactions in a model community consisting of 20 strains of naturally co-occurring soil bacteria. We find that the interaction network is strongly hierarchical and lacks significant non-transitive motifs, a result that is robust across multiple environments. Moreover, in agreement with recently proposed community assembly rules, the full 20-strain competition resulted in extinction of all but three of the most highly competitive strains, indicating that higher order interactions do not play a major role in structuring this community. The lack of non-transitivity and higher order interactions in vitro indicates that other factors, such as temporal or spatial heterogeneity, must be at play in enabling these strains to coexist in nature.

Journal ArticleDOI
TL;DR: Results of the intervention scores indicate that the method proposed in this study can provide new effective combinations of Chinese herbal medicines for T2DM, and can effectively promote the modernization and development of TCM.

Journal ArticleDOI
TL;DR: It is proved that the problem the authors define is NP-hard, and efficient algorithms are provided by adapting techniques for finding dense subgraphs whose edges occur in short time intervals.
Abstract: Online social networks are often defined by considering interactions of entities at an aggregate level. For example, a call graph is formed among individuals who have called each other at least once; or at least k times. Similarly, in social-media platforms, we consider implicit social networks among users who have interacted in some way, e.g., have made a conversation, have commented to the content of each other, and so on. Such definitions have been used widely in the literature and they have offered significant insights regarding the structure of social networks. However, it is obvious that they suffer from a severe limitation: They neglect the precise time that interactions among the network entities occur. In this article, we consider interaction networks, where the data description contains not only information about the underlying topology of the social network, but also the exact time instances that network entities interact. In an interaction network, an edge is associated with a timestamp, and multiple edges may occur for the same pair of entities. Consequently, interaction networks offer a more fine-grained representation, which can be leveraged to reveal otherwise hidden dynamic phenomena. In the setting of interaction networks, we study the problem of discovering dynamic dense subgraphs whose edges occur in short time intervals. We view such subgraphs as fingerprints of dynamic activity occurring within network communities. Such communities represent groups of individuals who interact with each other in specific time instances, for example, a group of employees who work on a project and whose interaction intensifies before certain project milestones. We prove that the problem we define is NP-hard, and we provide efficient algorithms by adapting techniques for finding dense subgraphs. We also show how to speed-up the proposed methods by exploiting concavity properties of our objective function and by the means of fractional programming. We perform extensive evaluation of the proposed methods on synthetic and real datasets, which demonstrates the validity of our approach and shows that our algorithms can be used to obtain high-quality results.

Journal ArticleDOI
TL;DR: NAP is a comprehensive web tool to automate network profiling and intra/inter-network topology comparison, designed to bridge the gap between network analysis, statistics, graph theory and partially visualization in a user-friendly way.
Abstract: Nowadays, due to the technological advances of high-throughput techniques, Systems Biology has seen a tremendous growth of data generation. With network analysis, looking at biological systems at a higher level in order to better understand a system, its topology and the relationships between its components is of a great importance. Gene expression, signal transduction, protein/chemical interactions, biomedical literature co-occurrences, are few of the examples captured in biological network representations where nodes represent certain bioentities and edges represent the connections between them. Today, many tools for network visualization and analysis are available. Nevertheless, most of them are standalone applications that often (i) burden users with computing and calculation time depending on the network’s size and (ii) focus on handling, editing and exploring a network interactively. While such functionality is of great importance, limited efforts have been made towards the comparison of the topological analysis of multiple networks. Network Analysis Provider (NAP) is a comprehensive web tool to automate network profiling and intra/inter-network topology comparison. It is designed to bridge the gap between network analysis, statistics, graph theory and partially visualization in a user-friendly way. It is freely available and aims to become a very appealing tool for the broader community. It hosts a great plethora of topological analysis methods such as node and edge rankings. Few of its powerful characteristics are: its ability to enable easy profile comparisons across multiple networks, find their intersection and provide users with simplified, high quality plots of any of the offered topological characteristics against any other within the same network. It is written in R and Shiny, it is based on the igraph library and it is able to handle medium-scale weighted/unweighted, directed/undirected and bipartite graphs. NAP is available at http://bioinformatics.med.uoc.gr/NAP .

Journal ArticleDOI
TL;DR: It is shown that disrupted networks contained topological network modules (TNMs) with shared properties that mapped onto distinct locations in networks, which provide new insight into networks by capturing proteins from different categories.
Abstract: Biological networks consist of functional modules, however detecting and characterizing such modules in networks remains challenging. Perturbing networks is one strategy for identifying modules. Here we used an advanced mathematical approach named topological data analysis (TDA) to interrogate two perturbed networks. In one, we disrupted the S. cerevisiae INO80 protein interaction network by isolating complexes after protein complex components were deleted from the genome. In the second, we reanalyzed previously published data demonstrating the disruption of the human Sin3 network with a histone deacetylase inhibitor. Here we show that disrupted networks contained topological network modules (TNMs) with shared properties that mapped onto distinct locations in networks. We define TMNs as proteins that occupy close network positions depending on their coordinates in a topological space. TNMs provide new insight into networks by capturing proteins from different categories including proteins within a complex, proteins with shared biological functions, and proteins disrupted across networks.

Journal ArticleDOI
TL;DR: Characteristics of host-pathogen protein interactions are investigated by presenting a comprehensive review of computational approaches applied on different infectious diseases by analyzing protein interactions between host and pathogen.
Abstract: Infection and disease progression is the outcome of protein interactions between pathogen and host Pathogen, the role player of Infection, is becoming a severe threat to life as because of its adaptability toward drugs and evolutionary dynamism in nature Identifying protein targets by analyzing protein interactions between host and pathogen is the key point Proteins with higher degree and possessing some topologically significant graph theoretical measures are found to be drug targets On the other hand, exceptional nodes may be involved in infection mechanism because of some pathway process and biologically unknown factors In this article, we attempt to investigate characteristics of host-pathogen protein interactions by presenting a comprehensive review of computational approaches applied on different infectious diseases As an illustration, we have analyzed a case study on infectious disease malaria, with its causative agent Plasmodium falciparum acting as 'Bait' and host, Homo sapiens/human acting as 'Prey' In this pathogen-host interaction network based on some interconnectivity and centrality properties, proteins are viewed as central, peripheral, hub and non-hub nodes and their significance on infection process Besides, it is observed that because of sparseness of the pathogen and host interaction network, there may be some topologically unimportant but biologically significant proteins, which can also act as Bait/Prey So, functional similarity or gene ontology mapping can help us in this case to identify these proteins