Network Construction Method Research Articles

Computational approaches for exploring the potential synergy and polypharmacology of Duhuo Jisheng Decoction in the therapy of osteoarthritis

The Duhuo Jisheng Decoction (DHJSD), a traditional Chinese medicine (TCM), has been widely used to treat osteoarthritis (OA). However, the mechanisms of action of the DHJSD have not been clearly elucidated. In the present study, the compounds in the DHJSD were characterized by three computational methods; the ligand clustering, chemical space distribution and network construction and analysis methods. The compounds that formed the medical composition of the DHJSD were divided into 10 clusters and possessed a broad diversity in chemical space distribution. The compounds also had the same coverage of chemical space as the OA drug/drug‑like compounds from DrugBank. In addition, multiple active compounds were identified as able to target multiple proteins in the drug‑target association networks (D‑T networks). A certain number of key compounds in the D‑T networks have been previously reported in the literature. The present study also constructed drug‑drug association networks (D‑D networks) and classified the DHJSD compounds into five clusters. The clusters represented multiple diverse combinations binding to the OA targets. These results suggested that the DHJSD had drug‑ and lead‑like compounds with potential synergy and polypharmacology against OA.

Canonical correlation analysis for RNA-seq co-expression networks

Digital transcriptome analysis by next-generation sequencing discovers substantial mRNA variants. Variation in gene expression underlies many biological processes and holds a key to unravelling mechanism of common diseases. However, the current methods for construction of co-expression networks using overall gene expression are originally designed for microarray expression data, and they overlook a large number of variations in gene expressions. To use information on exon, genomic positional level and allele-specific expressions, we develop novel component-based methods, single and bivariate canonical correlation analysis, for construction of co-expression networks with RNA-seq data. To evaluate the performance of our methods for co-expression network inference with RNA-seq data, they are applied to lung squamous cell cancer expression data from TCGA database and our bipolar disorder and schizophrenia RNA-seq study. The preliminary results demonstrate that the co-expression networks constructed by canonical correlation analysis and RNA-seq data provide rich genetic and molecular information to gain insight into biological processes and disease mechanism. Our new methods substantially outperform the current statistical methods for co-expression network construction with microarray expression data or RNA-seq data based on overall gene expression levels.

Protein co-expression network analysis (ProCoNA).

BackgroundBiological networks are important for elucidating disease etiology due to their ability to model complex high dimensional data and biological systems. Proteomics provides a critical data source for such models, but currently lacks robust de novo methods for network construction, which could bring important insights in systems biology.ResultsWe have evaluated the construction of network models using methods derived from weighted gene co-expression network analysis (WGCNA). We show that approximately scale-free peptide networks, composed of statistically significant modules, are feasible and biologically meaningful using two mouse lung experiments and one human plasma experiment. Within each network, peptides derived from the same protein are shown to have a statistically higher topological overlap and concordance in abundance, which is potentially important for inferring protein abundance. The module representatives, called eigenpeptides, correlate significantly with biological phenotypes. Furthermore, within modules, we find significant enrichment for biological function and known interactions (gene ontology and protein-protein interactions).ConclusionsBiological networks are important tools in the analysis of complex systems. In this paper we evaluate the application of weighted co-expression network analysis to quantitative proteomics data. Protein co-expression networks allow novel approaches for biological interpretation, quality control, inference of protein abundance, a framework for potentially resolving degenerate peptide-protein mappings, and a biomarker signature discovery.

Maximizing capture of gene co-expression relationships through pre-clustering of input expression samples: an Arabidopsis case study

BackgroundIn genomics, highly relevant gene interaction (co-expression) networks have been constructed by finding significant pair-wise correlations between genes in expression datasets. These networks are then mined to elucidate biological function at the polygenic level. In some cases networks may be constructed from input samples that measure gene expression under a variety of different conditions, such as for different genotypes, environments, disease states and tissues. When large sets of samples are obtained from public repositories it is often unmanageable to associate samples into condition-specific groups, and combining samples from various conditions has a negative effect on network size. A fixed significance threshold is often applied also limiting the size of the final network. Therefore, we propose pre-clustering of input expression samples to approximate condition-specific grouping of samples and individual network construction of each group as a means for dynamic significance thresholding. The net effect is increase sensitivity thus maximizing the total co-expression relationships in the final co-expression network compendium.ResultsA total of 86 Arabidopsis thaliana co-expression networks were constructed after k-means partitioning of 7,105 publicly available ATH1 Affymetrix microarray samples. We term each pre-sorted network a Gene Interaction Layer (GIL). Random Matrix Theory (RMT), an un-supervised thresholding method, was used to threshold each of the 86 networks independently, effectively providing a dynamic (non-global) threshold for the network. The overall gene count across all GILs reached 19,588 genes (94.7% measured gene coverage) and 558,022 unique co-expression relationships. In comparison, network construction without pre-sorting of input samples yielded only 3,297 genes (15.9%) and 129,134 relationships. in the global network.ConclusionsHere we show that pre-clustering of microarray samples helps approximate condition-specific networks and allows for dynamic thresholding using un-supervised methods. Because RMT ensures only highly significant interactions are kept, the GIL compendium consists of 558,022 unique high quality A. thaliana co-expression relationships across almost all of the measurable genes on the ATH1 array. For A. thaliana, these networks represent the largest compendium to date of significant gene co-expression relationships, and are a means to explore complex pathway, polygenic, and pleiotropic relationships for this focal model plant. The networks can be explored at sysbio.genome.clemson.edu. Finally, this method is applicable to any large expression profile collection for any organism and is best suited where a knowledge-independent network construction method is desired.

Hierarchical Settlement Networks

A network representation is introduced for visualizing hierarchical region structures on various spatial scales. The method is based on a spring-block model approach borrowed from physics and it was previously used with success to detect regions in any geographical space. We illustrate here our network construction method for the case of Transylvania, USA and Hungary.

Evaluation of gene association methods for coexpression network construction and biological knowledge discovery.

BackgroundConstructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical.Methods and ResultsIn this study, we compared eight gene association methods – Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding's D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson – and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods.ConclusionsWe found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction.

An Energy Efficient Cluster Formation and Maintenance Scheme for Wireless Sensor Networks

Nowadays, wireless sensor networks (WSNs) comprise a tremendously growing infrastructure for monitoring the physical or environmental conditions of objects. WSNs pose challenges to mitigating energy dissipation by constructing a reliable and energy saving network. In this paper, we propose a novel network construction and routing method by defining three different duties for sensor nodes, that is, node gateways, cluster heads, and cluster members, and then by applying a hierarchical structure from the sink to the normal sensing nodes. This method provides an efficient rationale to support the maximum coverage, to recover missing data with node mobility, and to reduce overall energy dissipation. All this should lengthen the lifetime of the network significantly.

Model averaging strategies for structure learning in Bayesian networks with limited data

BackgroundConsiderable progress has been made on algorithms for learning the structure of Bayesian networks from data. Model averaging by using bootstrap replicates with feature selection by thresholding is a widely used solution for learning features with high confidence. Yet, in the context of limited data many questions remain unanswered. What scoring functions are most effective for model averaging? Does the bias arising from the discreteness of the bootstrap significantly affect learning performance? Is it better to pick the single best network or to average multiple networks learnt from each bootstrap resample? How should thresholds for learning statistically significant features be selected?ResultsThe best scoring functions are Dirichlet Prior Scoring Metric with small λ and the Bayesian Dirichlet metric. Correcting the bias arising from the discreteness of the bootstrap worsens learning performance. It is better to pick the single best network learnt from each bootstrap resample. We describe a permutation based method for determining significance thresholds for feature selection in bagged models. We show that in contexts with limited data, Bayesian bagging using the Dirichlet Prior Scoring Metric (DPSM) is the most effective learning strategy, and that modifying the scoring function to penalize complex networks hampers model averaging. We establish these results using a systematic study of two well-known benchmarks, specifically ALARM and INSURANCE. We also apply our network construction method to gene expression data from the Cancer Genome Atlas Glioblastoma multiforme dataset and show that survival is related to clinical covariates age and gender and clusters for interferon induced genes and growth inhibition genes.ConclusionsFor small data sets, our approach performs significantly better than previously published methods.

A Novel Scale-free Network Construction Method and Its Application in Gene Expression Profiles Simulation*

In this paper,a novel scale-free network construction algorithm based on reconnection method was proposed.The regulatory node of the new node will be reselected according to the reconnection method.The probability of reconnection depends on the gamma in the power-law distribution model parameters.The constructed network with our algorithm was used for simulating gene expression profiles using differential equation model with two heuristic search algorithms,GA and PSO,and new algorithm GFA to optimize the criterion.The candidate old node can be selected as regulatory node based on the number of links the old node already has.The network in the experiment was testified using log-log graph.And the simulated gene expression profiles were also tested with three different well developed algorithms' software available free from internet by reconstructing the network.PPV and Se of the links were calculated and visualized.A part of the results and the full version program written by java could be downloaded from our website: http://ccst.jlu.edu.cn/CSBG/ourown/.

Minimizing interference in ad hoc networks with bounded communication radius

We consider a topology control problem in which we are given a set of sensors in Rd and we would like to assign a communication radius to each of them so that they generate a connected network and have low receiver-based interference (defined as the largest in-degree of the network). We show that any radii assignment that generates a connected network can be modified so that interference is (asymptotically) unaffected and no sensor is assigned communication radius larger than Rmin, where Rmin is the smallest possible radius needed to obtain strong connectivity. Combining this result with the previous network construction methods (see Halldórsson and Tokuyama (2008) [7] and von Rickenbach et al. (2009) [11]), we obtain a way to construct a connected network of low interference and bounded radii. Since the radius of a sensor is only affected by neighboring sensors, this construction can be done in a distributed fashion.

Molecular ecological network analyses.

BackgroundUnderstanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data.ResultsHere, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA).ConclusionsThe RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.

Network Characteristics Analysis of Ad hoc Network for APTS

Based on actual demands and application status of Advanced Public Transportation System (APTS) in the civil city, and the full use of the technique, such as the internet of things, GPS and etc. In the paper, a solution is proposed for APTS, which is composed of data transmission protocol and ad hoc network construction method based on the bus location. Together with the movement characteristic of ad hoc network node on bus, node dynamic mobile model of link break rate and link changes queuing model are build, and dynamic performance of the network is also priority analyzed. Finally, in the simulation experiment on Qualnet plat, it verifies the close relationship between the dynamic network characteristics and link break rate of Ad hoc network. The speed of the public bus, the distribution density of the Ad hoc network node and long distance transmission are effectively combined by the link rate model, to analysis the dynamic characteristics of the network and link break rate in theory. It will provide the theoretical basis and data support for the ad hoc network of APTS.

Social influence and spread dynamics in social networks.

Social networks often serve as a critical medium for information dissemination, diffusion of epidemics, and spread of behavior, by shared activities or similarities between individuals. Recently, we have witnessed an explosion of interest in studying social influence and spread dynamics in social networks. To date, relatively little material has been provided on a comprehensive review in this field. This brief survey addresses this issue. We present the current significant empirical studies on real social systems, including network construction methods, measures of network, and newly empirical results. We then provide a concise description of some related social models from both macro- and micro-level perspectives. Due to the difficulties in combining real data and simulation data for verifying and validating real social systems, we further emphasize the current research results of computational experiments. We hope this paper can provide researchers significant insights into better understanding the characteristics of personal influence and spread patterns in large-scale social systems.

A construction method of matroidal networks

This paper discusses the question whether a network-matroid construction can be modified so that all matroid dependencies are reflected in the network. We propose a novel construction method for a matroidal network to address this issue. Furthermore, we investigate some properties of the matroidal network based on our construction method. New relations between the matroidal network and its associated matroid are developed through various examples.

Network for Activation of Human Endothelial Cells by Oxidized Phospholipids

Rationale: Oxidized palmitoyl arachidonyl phosphatidylcholine (Ox-PAPC) accumulates in atherosclerotic lesions, is proatherogenic, and influences the expression of more than 1000 genes in endothelial cells. Objective: To elucidate the major pathways involved in Ox-PAPC action, we conducted a systems analysis of endothelial cell gene expression after exposure to Ox-PAPC. Methods and Results: We used the variable responses of primary endothelial cells from 149 individuals exposed to Ox-PAPC to construct a network that consisted of 11 groups of genes, or modules. Modules were enriched for a broad range of Gene Ontology pathways, some of which have not been identified previously as major Ox-PAPC targets. Further validating our method of network construction, modules were consistent with relationships established by cell biology studies of Ox-PAPC effects on endothelial cells. This network provides novel hypotheses about molecular interactions, as well as candidate molecular regulators of inflammation and atherosclerosis. We validated several hypotheses based on network connections and genomic association. Our network analysis predicted that the hub gene CHAC1 (cation transport regulator homolog 1) was regulated by the ATF4 (activating transcription factor 4) arm of the unfolded protein response pathway, and here we showed that ATF4 directly activates an element in the CHAC1 promoter. We showed that variation in basal levels of heme oxygenase 1 (HMOX1) contribute to the response to Ox-PAPC, consistent with its position as a hub in our network. We also identified G-protein–coupled receptor 39 (GPR39) as a regulator of HMOX1 levels and showed that it modulates the promoter activity of HMOX1. We further showed that OKL38/OSGN1 (oxidative stress–induced growth inhibitor), the hub gene in the blue module, is a key regulator of both inflammatory and antiinflammatory molecules. Conclusions: Our systems genetics approach has provided a broad view of the pathways involved in the response of endothelial cells to Ox-PAPC and also identified novel regulatory mechanisms.

Meta analysis of Chronic Fatigue Syndrome through integration of clinical, gene expression, SNP and proteomic data.

We start by constructing gene-gene association networks based on about 300 genes whose expression values vary between the groups of CFS patients (plus control). Connected components (modules) from these networks are further inspected for their predictive ability for symptom severity, genotypes of two single nucleotide polymorphisms (SNP) known to be associated with symptom severity, and intensity of the ten most discriminative protein features. We use two different network construction methods and choose the common genes identified in both for added validation. Our analysis identified eleven genes which may play important roles in certain aspects of CFS or related symptoms. In particular, the gene WASF3 (aka WAVE3) possibly regulates brain cytokines involved in the mechanism of fatigue through the p38 MAPK regulatory pathway.

A High-Resolution C. elegans Essential Gene Network Based on Phenotypic Profiling of a Complex Tissue

High-content screening for gene profiling has generally been limited to single cells. Here, we explore an alternative approach-profiling gene function by analyzing effects of gene knockdowns on the architecture of a complex tissue in a multicellular organism. We profile 554 essential C. elegans genes by imaging gonad architecture and scoring 94 phenotypic features. To generate a reference for evaluating methods for network construction, genes were manually partitioned into 102 phenotypic classes, predicting functions for uncharacterized genes across diverse cellular processes. Using this classification as a benchmark, we developed a robust computational method for constructing gene networks from high-content profiles based on a network context-dependent measure that ranks the significance of links between genes. Our analysis reveals that multi-parametric profiling in a complex tissue yields functional maps with a resolution similar to genetic interaction-based profiling in unicellular eukaryotes-pinpointing subunits of macromolecular complexes and components functioning in common cellular processes.

Zigbee-Based Positioning System For Coal Miners

China's present communication systems in coal mine can not effectively provide the information such as dynamic distribution of coal miners in work and production environment. Hence, rescue efforts are hard to be carried out in case of coal mine accident. To deal with this problem, the paper at first introduces the characteristics, structure and network construction method of ZigBee protocol. Then, by using the ZigBee technology based on IEEE802.15.4 criteria, the wireless communication network and the structure of ZigBee communication module for coal mine are studied and designed. At last, multilateration positioning algorithm is employed to realize the positioning of coal miners. Coal miner positioning system enters into computer management system via network, laying important actual significance to the production safety and emergent resuce of coal mine.

A physically based watershed partitioning method

A new contour-based watershed partitioning method, relying on tracking flow lines moving from upslope to downslope, is presented. The model works by using only contour data and consists of three principal steps: 1) Ideal Drainage Network tracking; 2) hillslope and channel elements building; and 3) actual contour-based element network construction. It automatically detects and manages sinks, peaks and saddles and calculates element attributes taking into account the non-planar shape of the elements. Watershed, subwatershed and draining area at any point can be automatically detected by using network elements interconnectivity. It is also possible to determine the Actual Drainage Network. The model is able to manage point data set trough interpolation, according with specific procedures. Also grid datasets can be used as input, the model is in fact provided with an overlapping algorithm for matching grid datasets to elements. Comparisons with an existing contour-based element network construction method show a better and more physically based description of the element network particularly useful for hydrological and mass movements' analyses.

A fast multi-output RBF neural network construction method

This paper investigates the center selection of multi-output radial basis function (RBF) networks, and a multi-output fast recursive algorithm (MFRA) is proposed. This method can not only reveal the significance of each candidate center based on the reduction in the trace of the error covariance matrix, but also can estimate the network weights simultaneously using a back substitution approach. The main contribution is that the center selection procedure and the weight estimation are performed within a well-defined regression context, leading to a significantly reduced computational complexity. The efficiency of the algorithm is confirmed by a computational complexity analysis, and simulation results demonstrate its effectiveness.