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At least 613 records · Page 34Linked to original sources
PubMed2026

Qingfei Dayuan granules alleviate the inflammatory response in lipopolysaccharide-induced acute lung injury mice by inhibiting the Nf-κB signaling pathway and regulating the complement pathway.

OBJECTIVES: The study aimed to explore the effects and mechanisms by which Qingfei Dayuan granules (QFDY) mitigate pulmonary inflammation in lipopolysaccharide (LPS)-induced acute lung injury (ALI). METHODS: We established an ALI mouse model by intraperitoneal injection of LPS. HE, Transmission electron microscopy, ELISA assay of inflammatory cytokines, and immunohistochemistry (IHC) were used to assess the degree of lung injury and inflammation. Utilizing network analysis and proteomics analysis, the potential targets and pathways of QFDY were identified. Western blot, IHC, and qRT-PCR analysis were used to evaluate the potential mechanism of QFDY. Additionally, the chemical composition of QFDY were performed using UPLC-MS/MS. KEY FINDINGS: QFDY reduced the pathologic changes and inflammatory cell infiltration in lung tissue inflammation. Network and proteomic analysis showed that the mechanism of QFDY protection against ALI is closely related to the Nuclear factor-kappa B (NF-κB) signaling pathway and complement pathway. Animal experiments showed that Qingfei Dayuan granules (QFDY) significantly reduced the levels of IL-1β, IL-6, TNF-α, and lung tissue F4/80-positive alveolar macrophages. Additionally, western blot and qRT-PCR analyses showed the inhibition of the NF-κB pathway. Notably, the levels of mannose-binding lectin (MBL2) were significantly increased, while complement C3a and complement C5a proteins were reduced in the QFDY group compared to the LPS group. CONCLUSIONS: QFDY suppressed the inflammation in LPS-induced ALI by inhibiting the NF-κB and complement pathway.

Animals↗
PubMed2002

Characterization and prediction of linker sequences of multi-domain proteins by a neural network.

In this paper, we describe a neural network analysis of sequences connecting two protein domains (domain linkers). The neural network was trained to distinguish between domain linker sequences and non-linker sequences, using a SCOP-defined domain library. The analysis indicated that a significant difference existed between domain linkers and non-linker regions, including intra-domain loop regions. Moreover, the resulting Hinton diagram showed a position-dependent amino acid preference of the domain linker sequences, and implied their non-random nature. We then applied the neural network to predict domain linkers in multi-domain protein sequences. As the result of a Jack-knife test, 58% of the predicted regions matched actual linker regions (specificity), and 36% of the SCOP-derived domain linkers were predicted (sensitivity). This prediction efficiency is superior to simpler methods derived from secondary structure prediction that assume that long loop regions are putative domain linkers. Altogether, these results suggest that domain linkers possess local characteristics different from those of loop regions.

Amino Acid Sequence↗
PubMed2006

Evolutionary dynamics of prokaryotic transcriptional regulatory networks.

The structure of complex transcriptional regulatory networks has been studied extensively in certain model organisms. However, the evolutionary dynamics of these networks across organisms, which would reveal important principles of adaptive regulatory changes, are poorly understood. We use the known transcriptional regulatory network of Escherichia coli to analyse the conservation patterns of this network across 175 prokaryotic genomes, and predict components of the regulatory networks for these organisms. We observe that transcription factors are typically less conserved than their target genes and evolve independently of them, with different organisms evolving distinct repertoires of transcription factors responding to specific signals. We show that prokaryotic transcriptional regulatory networks have evolved principally through widespread tinkering of transcriptional interactions at the local level by embedding orthologous genes in different types of regulatory motifs. Different transcription factors have emerged independently as dominant regulatory hubs in various organisms, suggesting that they have convergently acquired similar network structures approximating a scale-free topology. We note that organisms with similar lifestyles across a wide phylogenetic range tend to conserve equivalent interactions and network motifs. Thus, organism-specific optimal network designs appear to have evolved due to selection for specific transcription factors and transcriptional interactions, allowing responses to prevalent environmental stimuli. The methods for biological network analysis introduced here can be applied generally to study other networks, and these predictions can be used to guide specific experiments.

Amino Acid Motifs↗
PubMed2006

The effects of incomplete protein interaction data on structural and evolutionary inferences.

BACKGROUND: Present protein interaction network data sets include only interactions among subsets of the proteins in an organism. Previously this has been ignored, but in principle any global network analysis that only looks at partial data may be biased. Here we demonstrate the need to consider network sampling properties explicitly and from the outset in any analysis. RESULTS: Here we study how properties of the yeast protein interaction network are affected by random and non-random sampling schemes using a range of different network statistics. Effects are shown to be independent of the inherent noise in protein interaction data. The effects of the incomplete nature of network data become very noticeable, especially for so-called network motifs. We also consider the effect of incomplete network data on functional and evolutionary inferences. CONCLUSION: Crucially, when only small, partial network data sets are considered, bias is virtually inevitable. Given the scope of effects considered here, previous analyses may have to be carefully reassessed: ignoring the fact that present network data are incomplete will severely affect our ability to understand biological systems.

Evolution, Molecular↗
PubMed1998

Application of artificial neural networks in HPLC method development.

The use of artificial neural networks (ANNs) for response surface modelling in HPLC method development for amiloride and methychlothiazide separation is reported. The independent input variables were pH and methanol percentage in mobile phase. The outputs were capacity factors. The results were compared with a statistical method (multiple nonlinear regression analysis). Networks were able to predict the experimental responses more accurately than the regression analysis.

Amiloride↗
PubMed2004

Molecular epidemiology of hepatitis C virus in a social network of injection drug users.

BACKGROUND: We aimed to measure the overlap between the social networks of injection drug users (IDUs) and the patterns of related hepatitis C virus (HCV) infections among IDUs. METHODS: A cohort of 199 IDUs (138 of whom were HCV RNA positive) was recruited from a local drug scene in Melbourne, Australia, and was studied using social network analysis and molecular phylogenetic analysis of 2 regions of the HCV genome. RESULTS: Eighteen clusters of related infections involving 51 IDUs (37.0% of HCV RNA-positive IDUs) were detected; these clusters could be separated into 66 discrete pairs. Twelve (18.2%) of the 66 IDU pairs with related infections reported having previously injected drugs together; conversely, only 12 (3.8%) of the 313 pairs of HCV RNA-positive IDUs who were injection partners had strong molecular evidence of related infections. The social and genetic distances that separated IDUs with identical genotypes were weakly associated. Significant clusters of phylogenetically related sequences identified from core region analysis persisted in the analysis of the nonstructural 5a protein region. Genotyping and sequence analysis revealed 2 mixed-genotype infections. CONCLUSIONS: Static social network methods are likely to gather information about a minority of patterns of HCV transmission, because of the difficulty of determining historical infection pathways in an established social network of IDUs. Nevertheless, molecular epidemiological methods identified clusters of IDUs with related viruses and provided information about mixed-genotype infection status.

Australia↗
PubMed2002

Machine learning of functional class from phenotype data.

MOTIVATION: Mutant phenotype growth experiments are an important novel source of functional genomics data which have received little attention in bioinformatics. We applied supervised machine learning to the problem of using phenotype data to predict the functional class of Open Reading Frames (ORFs) in Saccaromyces cerevisiae. Three sources of data were used: TRansposon-Insertion Phenotypes, Localization and Expression in Saccharomyces (TRIPLES), European Functional Analysis Network (EUROFAN) and Munich Information Center for Protein Sequences (MIPS). The analysis of the data presented a number of challenges to machine learning: multi-class labels, a large number of sparsely populated classes, the need to learn a set of accurate rules (not a complete classification), and a very large amount of missing values. We modified the algorithm C4.5 to deal with these problems. RESULTS: Rules were learnt which are accurate and biologically meaningful. The rules predict function of 83 ORFs of unknown function at an estimated accuracy of > or = 80%.

Artificial Intelligence↗
PubMed2002

The people who make organizations go--or stop.

Managers invariably use their personal contacts when they need to, say, meet an impossible deadline or learn the truth about a new boss. Increasingly, it's through these informal networks--not just through traditional organizational hierarchies--that information is found and work gets done. But to many senior executives, informal networks are unobservable and ungovernable--and, therefore, not amenable to the tools of management. As a result, executives tend to work around informal networks or, worse, try to ignore them. When they do acknowledge the networks' existence, executives fall back on intuition--scarcely a dependable tool--to guide them in nurturing this social capital. It doesn't have to be that way. It is entirely possible to develop and manage informal networks systematically, say management experts Cross and Prusak. Specifically, senior executives need to focus their attention on four key role-players in informal networks: Central connectors link most employees in an informal network with one another; they provide the critical information or expertise that the entire network draws on to get work done. Boundary spanners connect an informal network with other parts of the company or with similar networks in other organizations. Information brokers link different subgroups in an informal network; if they didn't, the network would splinter into smaller, less effective segments. And finally, there are peripheral specialists, who anyone in an informal network can turn to for specialized expertise but who work apart from most people in the network. The authors describe the four roles in detail, discuss the use of a well-established tool called social network analysis for determining who these role-players are in the network, and suggest ways that executives can transform ineffective informal networks into productive ones.

Administrative Personnel↗
PubMed2003

Influence of information sources on the adoption of uterine fibroid embolization by interventional radiologists.

OBJECTIVES: The purpose of the research was to (1) understand the influence of information sources on the awareness and adoption of uterine fibroid embolization (UFE) by interventional radiologists in Michigan and (2) to decipher communication relations in the social network of interventional radiologists that were most conducive to the flow of information about UFE. METHODS: Diffusion of innovations theory and constructs in social network analysis formed the basis for the development of an interview guide. Thirty-two interventional radiologists in Michigan were interviewed over the phone. Chi-square statistics were employed to analyze the awareness and adoption of UFE. Factor analysis was applied to decipher important communication relations in the social network of interventional radiologists. RESULTS: Conferences were found to be an initial source of information, creating awareness among early adopters (P < 0.05), but other individuals were found to be influential sources in the adoption of UFE by later adopters (P < 0.05). Radiologists rarely browsed Websites for information. Work relations in everyday clinical practice were the communication relations most conducive to the flow of information about UFE. Preliminary qualitative data indicated that opinion leaders in the diffusion of UFE in Michigan were located in hospitals primarily dedicated to practice rather than in hospitals affiliated with universities. CONCLUSIONS: Journals are important information sources for creating awareness and stimulating adoption of innovation among both early and late adopters of new procedures in interventional radiology. Conferences, however, are significantly more important for creating early awareness, while interactions with colleagues is the most important factor in stimulating use of the innovation among later adopters. Among colleagues, opinion leaders in nonacademic hospitals may be more influential than individuals in the academic community.

Adult↗
PubMedDate not supplied

Balancing systematic and flexible exploration of social networks.

Social network analysis (SNA) has emerged as a powerful method for understanding the importance of relationships in networks. However, interactive exploration of networks is currently challenging because: (1) it is difficult to find patterns and comprehend the structure of networks with many nodes and links, and (2) current systems are often a medley of statistical methods and overwhelming visual output which leaves many analysts uncertain about how to explore in an orderly manner. This results in exploration that is largely opportunistic. Our contributions are techniques to help structural analysts understand social networks more effectively. We present SocialAction, a system that uses attribute ranking and coordinated views to help users systematically examine numerous SNA measures. Users can (1) flexibly iterate through visualizations of measures to gain an overview, filter nodes, and find outliers, (2) aggregate networks using link structure, find cohesive subgroups, and focus on communities of interest, and (3) untangle networks by viewing different link types separately, or find patterns across different link types using a matrix overview. For each operation, a stable node layout is maintained in the network visualization so users can make comparisons. SocialAction offers analysts a strategy beyond opportunism, as it provides systematic, yet flexible, techniques for exploring social networks.

Algorithms↗
PubMed2003

Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: insights from an identifiability analysis of an in silico network.

Gene expression profiles are an increasingly common data source that can yield insights into the functions of cells at a system-wide level. The present work considers the limitations in information content of gene expression data for reverse engineering regulatory networks. An in silico genetic regulatory network was constructed for this purpose. Using the in silico network, a formal identifiability analysis was performed that considered the accuracy with which the parameters in the network could be estimated using gene expression data and prior structural knowledge (which transcription factors regulate which genes) as a function of the input perturbation and stochastic gene expression. The analysis yielded experimentally relevant results. It was observed that, in addition to prior structural knowledge, prior knowledge of kinetic parameters, particularly mRNA degradation rate constants, was necessary for the network to be identifiable. Also, with the exception of cases where the noise due to stochastic gene expression was high, complex perturbations were more favorable for identifying the network than simple ones. Although the results may be specific to the network considered, the present study provides a framework for posing similar questions in other systems.

Computational Biology↗
PubMed1993

Regional cerebral blood flow estimation by neural network-based parametric regression analysis.

An artificial neural network (ANN) model was proposed for real-time estimation of regional cerebral blood flow (rCBF), by given head and expired air curves obtained through 133Xe inhalation. The network was constructed according to a regression model described by a linear differential equation. Experimental results compare well with those obtained by conventional curve fitting strategies, but the parameter estimation process is much simplified. A systematic procedure in developing ANN for parametric regression analysis was introduced; networks are constructed according to the selected regression model so that the obtained weights of a trained network directly represent parameters of the regression model which best fits the observed data set. Such a design-oriented methodology extends the classification-based applications of ANN to parametric regression analysis, and therefore may have more generalized applications besides rCBF estimation.

Algorithms↗
PubMed2005

Diversity and identity of mechanical properties of icosahedral viral capsids studied with elastic network normal mode analysis.

We analyze the mechanical properties and putative dynamical fluctuations of a variety of viral capsids comprising different sizes and quasi-equivalent symmetries by performing normal mode analysis using the elastic network model. The expansion of the capsid to a swollen state is studied using normal modes and is compared with the experimentally observed conformational change for three of the viruses for which experimental data exist. We show that a combination of one or two normal modes captures remarkably well the overall translation that dominates the motion between the two conformational states, and reproduces the overall conformational change. We observe for all of the viral capsids that the nature of the modes is different. In particular for the T=7 virus, HK97, for which the shape of the capsid changes from spherical to faceted polyhedra, two modes are necessary to accomplish the conformational transition. In addition, we extend our study to viral capsids with other T numbers, and discuss the similarities and differences in the features of virus capsid conformational dynamics. We note that the pentamers generally have higher flexibility and propensity to move freely from the other capsomers, which facilitates the shape adaptation that may be important in the viral life cycle.

Bacteriophages↗
PubMed2006

Efficient estimation of graphlet frequency distributions in protein-protein interaction networks.

MOTIVATION: Algorithmic and modeling advances in the area of protein-protein interaction (PPI) network analysis could contribute to the understanding of biological processes. Local structure of networks can be measured by the frequency distribution of graphlets, small connected non-isomorphic induced subgraphs. This measure of local structure has been used to show that high-confidence PPI networks have local structure of geometric random graphs. Finding graphlets exhaustively in a large network is computationally intensive. More complete PPI networks, as well as PPI networks of higher organisms, will thus require efficient heuristic approaches. RESULTS: We propose two efficient and scalable heuristics for finding graphlets in high-confidence PPI networks. We show that both PPI and their model geometric random networks, have defined boundaries that are sparser than the 'inner parts' of the networks. In addition, these networks exhibit 'uniformity' of local structure inside the networks. Our first heuristic exploits these two structural properties of PPI and geometric random networks to find good estimates of graphlet frequency distributions in these networks up to 690 times faster than the exhaustive searches. Our second heuristic is a variant of a more standard sampling technique and it produces accurate approximate results up to 377 times faster than the exhaustive searches. We indicate how the combination of these approaches may result in an even better heuristic. AVAILABILITY: Supplementary information is available at http://www.cs.toronto.edu/~natasha/BIOINF-2005-0946/Supplementary.pdf. Software implementing the algorithms is available at http://www.cs.toronto.edu/~natasha/BIOINF-2005-0946/estimate_grap-hlets.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Algorithms↗
PubMed2005

Role-similarity based functional prediction in networked systems: application to the yeast proteome.

We propose a general method to predict functions of vertices where (i) the wiring of the network is somehow related to the vertex functionality and (ii) a fraction of the vertices are functionally classified. The method is influenced by role-similarity measures of social network analysis. The two versions of our prediction scheme are tested on model networks where the functions of the vertices are designed to match their network surroundings. We also apply these methods to the proteome of the yeast Saccharomyces cerevisiae and find the results compatible with more specialized methods.

Animals↗
PubMed2006

Why do hubs tend to be essential in protein networks?

The protein-protein interaction (PPI) network has a small number of highly connected protein nodes (known as hubs) and many poorly connected nodes. Genome-wide studies show that deletion of a hub protein is more likely to be lethal than deletion of a non-hub protein, a phenomenon known as the centrality-lethality rule. This rule is widely believed to reflect the special importance of hubs in organizing the network, which in turn suggests the biological significance of network architectures, a key notion of systems biology. Despite the popularity of this explanation, the underlying cause of the centrality-lethality rule has never been critically examined. We here propose the concept of essential PPIs, which are PPIs that are indispensable for the survival or reproduction of an organism. Our network analysis suggests that the centrality-lethality rule is unrelated to the network architecture, but is explained by the simple fact that hubs have large numbers of PPIs, therefore high probabilities of engaging in essential PPIs. We estimate that approximately 3% of PPIs are essential in the yeast, accounting for approximately 43% of essential genes. As expected, essential PPIs are evolutionarily more conserved than nonessential PPIs. Considering the role of essential PPIs in determining gene essentiality, we find the yeast PPI network functionally more robust than random networks, yet far less robust than the potential optimum. These and other findings provide new perspectives on the biological relevance of network structure and robustness.

Binding Sites↗
PubMed2006

The health of nations in a global context: trade, global stratification, and infant mortality rates.

Despite the call for a better understanding of macro-level factors that affect population health, social epidemiological research has tended to focus almost exclusively on national-level factors, such as Gross Domestic Product per capita (GDP/c) or levels of social cohesion. Using a world-systems framework to examine cross-national variations in infant mortality, this paper seeks to emphasize the effects of global trade on national-level population health. Rather than viewing national-level health indicators as autonomous from broader global contexts, the study uses network analysis methods to examine the effects of international trade on infant mortality rates. Network data for countries were derived from international data on the trade of capital-intensive commodities in 2000. Using automorphic equivalence to measure the degree to which actors in a network perform similar roles, countries were assigned into one of six world-system blocks, each with its own pattern of trade. These blocks were dummy-coded and tested using ordinary least squares (OLS) regression. A key finding from this analysis is that after controlling for national-level factors, the two blocks with the lowest density in capital-intensive exchange, i.e., the periphery, are significantly and positively associated with national-level infant mortality rates. Results show the effects of peripherality and stratification on population health, and highlight the influence of broader macro-level factors such as trade and globalization on national health.

Commerce↗