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PubMed1995

Using social network analysis to study patterns of drug use among urban drug users at high risk for HIV/AIDS.

Few studies have examined the current social relationships of injecting drug users. This paper examines the structural and relationship characteristics of the social networks of injecting drug users, and the relation of social network characteristics to the HIV infection risk behavior of frequency of injecting heroin and cocaine. The study sample was comprised of 293 inner city injecting drug users in Baltimore, Maryland. Most participants (89%) reported at least one family member in their social network, and 44% listed their mother or step-mother in their network. Presence of family members in personal social networks was not related to patterns of drug use examined here; however, those who reported a partner in their personal social network injected significantly less often than those who did not report a partner. Network density and size of drug subnetworks were positively associated with frequency of drug injection. The results of this study suggest that social network analysis may be a useful tool for understanding the social context of HIV/AIDS risk behaviors.

Acquired Immunodeficiency Syndrome↗

PubMed2024

Decoding the genetic landscape of allergic rhinitis: a comprehensive network analysis revealing key genes and potential therapeutic targets.

BACKGROUND: Allergic Rhinitis (AR), an inflammatory affliction impacting the upper respiratory tract, has been registering a substantial surge in incidence across the globe. METHODS: We embarked on examination of differentially expressed genes (DEGs) and the Weighted Gene Co-Expression Network Analysis (WGCNA). With this armory of genes identified, we engaged the tools of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Our study continued with the establishment of a protein-protein interaction (PPI) network and the application of LASSO regression. Finally, we leveraged a docking model to elucidate potential drug-gene interactions involving these key genes. RESULTS: Through WGCNA and different express genes screening, PPI network was performed, identifying top 20 key genes, including CD44, CD69, CD274. LASSO regression identified three independent factors, STARD5, CST1, and CHAC1, that were significantly associated with AR. A predictive model was developed with an AUC value over 0.75. Also, 105 potential therapeutic agents were discovered, including Fluorouracil, Cyclophosphamide, Doxorubicin, and Hydrocortisone, offering promising therapeutic strategies for AR. CONCLUSION: By fuzing DEGs with key genes derived from WGCNA, this study has illuminated a comprehensive network of gene interactions involved in the pathogenesis of AR, paving the way for future biomarker and therapeutic target discovery in AR.

Humans↗

PubMed2001

Interorganizational relationships among HIV/AIDS service organizations in Baltimore: a network analysis.

A wide variety of organizations has become involved in providing medical and social services to people living with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS). Although there is much interest among policymakers, service providers, and clients in coordination among HIV/AIDS service organizations, few studies have used network analytic tools to examine existing systems of HIV-related care. In an effort to fill this gap, this study used network analysis methods to describe several aspects of the interorganizational relationships among 30 HIV/AIDS service agencies in Baltimore, Maryland. Client referrals to other organizations, client referrals from other organizations, exchange of information about shared clients, formal written linkage agreements for client referrals, and joint programs were each examined as a distinct type of network tie, with each the basis of a separate network among these 30 organizations. All of the networks except the one based on joint programs were relatively well connected, with most organizations either directly or indirectly linked. Most of the interorganizational collaboration occurred on a rather ad hoc basis for the purposes of meeting the more immediate needs presented by clients. Highly structured coordination involving substantial investment of resources and ongoing interagency activities appeared to be less common. The findings from this study also suggest that the providers in Baltimore tend to work directly with others as client needs arise rather than negotiating through "clearinghouse" types of organizations. Of the 30 HIV/AIDS service organizations, 5 were highly central in at least four of the five different types of networks. These five organizations--each having a critical role in the continuum of care--may be considered the most central core of the HIV/AIDS service delivery network in Baltimore. These organizations tend to be those that have been created specifically to provide HIV-related services or that specialize in HIV/AIDS care. This research can help policymakers understand how an HIV-related service delivery network may function and delineate key features of a network. In all communities, this type of assessment is critical to designing interventions to promote collaboration that are feasible within the context of existing interorganizational relationships. This type of data also has implications for informing activities to build the capacity of HIV/AIDS service organizations.

Acquired Immunodeficiency Syndrome↗

PubMed2006

Applying network analysis to the conservation of habitat trees in urban environments: a case study from Brisbane, Australia.

In Australia more than 300 vertebrates, including 43 insectivorous bat species, depend on hollows in habitat trees for shelter with many species using a network of multiple trees as roosts. We used roost-switching data on white-striped freetail bats (Tadarida australis; Microchiroptera: Molossidae) to construct a network representation of day roosts in suburban Brisbane, Australia. Bats were caught from a communal roost tree with a roosting group of several hundred individuals and released with transmitters. Each roost used by the bats represented a node in the network, and the movements of bats between roosts formed the links between nodes. Despite differences in gender and reproductive stages, the bats exhibited the same behavior throughout three radiotelemetry periods and over 500 bat days of radio tracking: each roosted in separate roosts, switched roosts very infrequently, and associated with other bats only at the communal roost. This network resembled a scale-free network in which the distribution of the number of links from each roost followed a power law. Despite being spread over a large geographic area (> 200 km2), each roost was connected to others by less than three links. One roost (the hub or communal roost) defined the architecture of the network because it had the most links. That the network showed scale-free properties has profound implications for the management of the habitat trees of this roosting group. Scale-free networks provide high tolerance against stochastic events such as random roost removals but are susceptible to the selective removal of hub nodes. Network analysis is a useful tool for understanding the structural organization of habitat tree usage and allows the informed judgment of the relative importance of individual trees and hence the derivation of appropriate management decisions. Conservation planners and managers should emphasize the differential importance of habitat trees and think of them as being analogous to vital service centers in human societies.

Animals↗

PubMed2005

A general framework for weighted gene co-expression network analysis.

Gene co-expression networks are increasingly used to explore the system-level functionality of genes. The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such networks. An important question is whether it is biologically meaningful to encode gene co-expression using binary information (connected=1, unconnected=0). We describe a general framework for ;soft' thresholding that assigns a connection weight to each gene pair. This leads us to define the notion of a weighted gene co-expression network. For soft thresholding we propose several adjacency functions that convert the co-expression measure to a connection weight. For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion). We generalize the following important network concepts to the case of weighted networks. First, we introduce several node connectivity measures and provide empirical evidence that they can be important for predicting the biological significance of a gene. Second, we provide theoretical and empirical evidence that the ;weighted' topological overlap measure (used to define gene modules) leads to more cohesive modules than its ;unweighted' counterpart. Third, we generalize the clustering coefficient to weighted networks. Unlike the unweighted clustering coefficient, the weighted clustering coefficient is not inversely related to the connectivity. We provide a model that shows how an inverse relationship between clustering coefficient and connectivity arises from hard thresholding. We apply our methods to simulated data, a cancer microarray data set, and a yeast microarray data set.

Journal Article↗

PubMed2004

Metabolic network analysis of lysine producing Corynebacterium glutamicum at a miniaturized scale.

We present a straightforward approach comprising (13)C tracer experiments at 200-microL volume in 96-well microtiter plates with on-line measurement of dissolved oxygen for quantitative high-throughput metabolic network analysis at a miniaturized scale. This method was successfully applied for cultivation and (13)C metabolic flux analysis of two mutants of lysine producing Corynebacterium glutamicum (ATCC 13287 and ATCC 21543). Microtiter-plate cultivations showed excellent accordance in kinetics and stoichiometry of growth and product formation as well as in intracellular flux distributions as compared with parallel shake-flask experiments. These cultivations further allowed clear identification of strain-specific flux differences such as increased flux toward lysine, increased flux through the pentose phosphate pathway (PPP), decreased flux through the tricarboxylic (TCA) cycle, and increased dihydroxyacetone formation in C. glutamicum ATCC 21543 compared with ATCC 13287. The present approach has strong potential for broad quantitative screening of metabolic network activities, especially those involving high-cost tracer substrates.

Carbon Isotopes↗

PubMed2006

Boolean network analysis of a neurotransmitter signaling pathway.

BACKGROUND: A Boolean network is a simple computational model that may provide insight into the overall behavior of genetic networks and is represented by variables with two possible states (on/off), of the individual nodes/genes of the network. In this study, a Boolean network model has been used to simulate a molecular pathway between two neurotransmitter receptor, dopamine and glutamate receptor, systems in order to understand the consequence of using logic gate rules between nodes, which have two possible states (active and inactive). RESULTS: The dynamical properties of this Boolean network model of the biochemical pathway shows that, the pathway is stable and that, deletion/knockout of certain biologically important nodes cause significant perturbation to this network. The analysis clearly shows that in addition to the expected components dopamine and dopamine receptor 2 (DRD2), Ca(2+) ions play a critical role in maintaining stability of the pathway. CONCLUSION: So this method may be useful for the identification of potential genetic targets, whose loss of function in biochemical pathways may be responsible for disease onset. The molecular pathway considered in this study has been implicated with a complex disorder like schizophrenia, which has a complex multifactorial etiology.

Animals↗

PubMed1997

"We decide, you carry it out": a social network analysis of multidisciplinary long-term care teams.

The purpose of this study was to describe the structure of multidisciplinary long-term care teams by identifying the pattern of relationships that develop amongst staff as they go about their work. Using a social network analysis approach, team members were classified as occupying the same structural position based on their patterns of relationships with other team members. The analysis was based on the results of a self-administered survey of 93 health care workers on three teams in the same multilevel geriatric care facility in Metropolitan Toronto. A common structure of the teams was identified consisting of two sub-teams: a multiprofessional sub-team and a nursing sub-team, each of which has a different structure indicating differential involvement in different types of teamwork. The multiprofessional sub-team has an "organic" structure and is mainly involved in teamwork that involves decision-making and problem-solving, whereas the nursing sub-team has a "mechanistic" structure and is mainly involved in task oriented work. The findings of this analysis indicate that while teamwork may be increasing the participation in decision-making by health professionals other than medicine, rather than flattening the hierarchical structure throughout the health care division of labour, its effects are limited to a group of higher status professionals. The clearly defined hierarchy remains for the lower status subdisciplines, and "I decide, you carry it out" has simply become "We decide, you carry it out".

Adult↗

PubMed2003

Proteomic profiling of urinary proteins in renal cancer by surface enhanced laser desorption ionization and neural-network analysis: identification of key issues affecting potential clinical utility.

Recent advances in proteomic profiling technologies, such as surface enhanced laser desorption ionization mass spectrometry, have allowed preliminary profiling and identification of tumor markers in biological fluids in several cancer types and establishment of clinically useful diagnostic computational models. There are currently no routinely used circulating tumor markers for renal cancer, which is often detected incidentally and is frequently advanced at the time of presentation with over half of patients having local or distant tumor spread. We have investigated the clinical utility of surface enhanced laser desorption ionization profiling of urine samples in conjunction with neural-network analysis to either detect renal cancer or to identify proteins of potential use as markers, using samples from a total of 218 individuals, and examined critical technical factors affecting the potential utility of this approach. Samples from patients before undergoing nephrectomy for clear cell renal cell carcinoma (RCC; n = 48), normal volunteers (n = 38), and outpatients attending with benign diseases of the urogenital tract (n = 20) were used to successfully train neural-network models based on either presence/absence of peaks or peak intensity values, resulting in sensitivity and specificity values of 98.3-100%. Using an initial "blind" group of samples from 12 patients with RCC, 11 healthy controls, and 9 patients with benign diseases to test the models, sensitivities and specificities of 81.8-83.3% were achieved. The robustness of the approach was subsequently evaluated with a group of 80 samples analyzed "blind" 10 months later, (36 patients with RCC, 31 healthy volunteers, and 13 patients with benign urological conditions). However, sensitivities and specificities declined markedly, ranging from 41.0% to 76.6%. Possible contributing factors including sample stability, changing laser performance, and chip variability were examined, which may be important for the long-term robustness of such approaches, and this study highlights the need for rigorous evaluation of such factors in future studies.

Adult↗

PubMed2002

Genealogy profiling through strain improvement by using metabolic network analysis: metabolic flux genealogy of several generations of lysine-producing corynebacteria.

A comprehensive approach of metabolite balancing, (13)C tracer studies, gas chromatography-mass spectrometry, matrix-assisted laser desorption ionization-time of flight mass spectrometry, and isotopomer modeling was applied for comparative metabolic network analysis of a genealogy of five successive generations of lysine-producing Corynebacterium glutamicum. The five strains examined (C. glutamicum ATCC 13032, 13287, 21253, 21526, and 21543) were previously obtained by random mutagenesis and selection. Throughout the genealogy, the lysine yield in batch cultures increased markedly from 1.2 to 24.9% relative to the glucose uptake flux. Strain optimization was accompanied by significant changes in intracellular flux distributions. The relative pentose phosphate pathway (PPP) flux successively increased, clearly corresponding to the product yield. Moreover, the anaplerotic net flux increased almost twofold as a consequence of concerted regulation of C(3) carboxylation and C(4) decarboxylation fluxes to cover the increased demand for lysine formation; thus, the overall increase was a consequence of concerted regulation of C(3) carboxylation and C(4) decarboxylation fluxes. The relative flux through isocitrate dehydrogenase dropped from 82.7% in the wild type to 59.9% in the lysine-producing mutants. In contrast to the NADPH demand, which increased from 109 to 172% due to the increasing lysine yield, the overall NADPH supply remained constant between 185 and 196%, resulting in a decrease in the apparent NADPH excess through strain optimization. Extrapolated to industrial lysine producers, the NADPH supply might become a limiting factor. The relative contributions of PPP and the tricarboxylic acid cycle to NADPH generation changed markedly, indicating that C. glutamicum is able to maintain a constant supply of NADPH under completely different flux conditions. Statistical analysis by a Monte Carlo approach revealed high precision for the estimated fluxes, underlining the fact that the observed differences were clearly strain specific.

Citric Acid Cycle↗

PubMedDate not supplied

Markov Network Analysis: suggestions for innovations in covariance structure analysis.

Studies of aging offer special methodological challenges to the researcher in that he must often examine the change of multiple correlated variables over time. We present a set of procedures that are specifically designed to model change in such multivariate situations. These procedures, which we will call Markov Network Analysis, are directly applicable to modeling change from longitudinal or serial data. In such cases, the parameters of the model have dynamic interpretations, e.g., as coefficients in positive or negative feedback loops. In cross-sectional data, one cannot directly estimate the dynamic coefficients but the model does show how certain dynamic interpretations can be made. Statistically, maximum likelihood estimation procedures are developed and presented. In the development of the statistical model, it is shown how the bias of sequential hypothesis testing, a frequent occurrence in the estimation of complex covariance structure models, may be reduced.

Age Factors↗

PubMed1996

A biomathematical model of intracranial arteriovenous malformations based on electrical network analysis: theory and hemodynamics.

Hemodynamics play a significant role in the propensity of intracranial arteriovenous malformations (AVMs) to hemorrhage and in influencing both therapeutic strategies and their complications. AVM hemodynamics are difficult to quantitate, particularly within or in close proximity to the nidus. Biomathematical models represent a theoretical method of investigating AVM hemodynamics but currently provide limited information because of the simplicity of simulated anatomic and physiological characteristics in available models. Our purpose was to develop a new detailed biomathematical model in which the morphological, biophysical, and hemodynamic characteristics of an intracranial AVM are replicated more faithfully. The technique of electrical network analysis was used to construct the biomathematical AVM model to provide an accurate rendering of transnidal and intranidal hemodynamics. The model represented a complex, noncompartmentalized AVM with 4 arterial feeders (with simulated pial and transdural supply), 2 draining veins, and a nidus consisting of 28 interconnecting plexiform and fistulous components. Simulated vessel radii were defined as observed in human AVMs. Common values were assigned for normal systemic arterial pressure, arterial feeder pressures, draining vein pressures, and central venous pressure. Using an electrical analogy of Ohm's law, flow was determined based on Poiseuille's law given the aforementioned pressures and resistances of each nidus vessel. Circuit analysis of the AVM vasculature based on the conservation of flow and voltage revealed the flow rate through each vessel in the AVM network. Once the flow rate was established, the velocity, the intravascular pressure gradient, and the wall shear stress were determined. Total volumetric flow through the AVM was 814 ml/min. Hemodynamic analysis of the AVM showed increased flow rate, flow velocity, and wall shear stress through the fistulous component. The intranidal flow rate varied from 5.5 to 57.0 ml/min with and average of 31.3 ml/min for the plexiform vessels and from 595.1 to 640.1 ml/min with an average of 617.6 ml/min for the fistulous component. The blood flow velocity through the AVM nidus ranged from 11.7 to 121.1 cm/s with an average of 66.4 cm/s for the plexiform vessels and from 446.9 to 480 dyne/cm2 with an average of 463.5 dyne/cm2 for the fistulous component. The wall shear stress ranged in magnitude from 33.2 to 342.1 dyne/cm2 with an average of 187.7 dyne/cm2 for the plexiform vessels and from 315.9 to 339.7 cm/s with an average of 327.8 cm/s for the fistulous component. The described novel biomathematical model characterizes the transnidal and intranidal hemodynamics of an intracranial AVM more accurately than was possible previously. This model should serve as a useful research tool for further theoretical investigations of intracranial AVMs and their hemodynamic sequelae.

Biophysical Phenomena↗

PubMed1975

Network analysis of dendritic fields of pyramidal cells in neocortex and Purkinje cells in the cerebellum of the rat.

The connectivity within the dendritic array of Purkinje cells in the cerebellum and pyramidal cells of the neocortex of the rat, stained by the Golgi-Cox method, has been quantified by the method of network analysis. Connectivity was characterized either by applying the system of Strahler ordering, which assigns a relative order of magnitude to each branch of the arborescence or by the identification of unique topological branching patterns within the tree. The former method has been used to define the entire dendritic array of the Purkinje cell and the apical system of neocortical pyramids. It has been shown that the relation between the numbers of branches of successive Strahler order in Purkinje cells form an inverse geometric series in which the highest order is unity and the ratio between successive orders approximates to 3. On the other hand, the apical dendrites of neocortical pyramids exhibit two bifurcation ratios, i.e. a ratio of 3 between low orders and a ratio of 4 between higher orders. A computer simulation technique was used to generate networks of a size comparable with the Purkinje cell networks and grown according to two hypotheses namely, a 'terminal growth model' in which additional segments were added randomly to the terminal branches only and a 'segmental growth model' in which additional segments were added randomly to any branch within the array including terminal branches. Subsequent ordering of the simulated trees revealed that the relation between the numbers of successive orders for networks generated according to the 'segmental model' tended towards an inverse geometric series with a ratio of 4 and that generated according to the 'terminal model' tended towards a ratio of 3. This result showed that the dendritic tree of Purkinje cells grow in a manner indistinguishable from a system adding branches to random terminal segments and that neocortical apical dendrites add their collateral branches to random segments of the apical shaft but that the collateral branches themselves grow by random terminal branching. The possibility that such conclusions may be influenced by loss of branches incurred by either a failure of impregnation, by sectioning, or by environmental influences was investigated by means of a computer technique...

Animals↗

PubMed2026

Nested co-expression network analysis identifies compact gene clusters in a black box.

MOTIVATION: Digital analysis of biological systems requires methods capable of identifying both broad and nested gene modules reflecting complex biological processes. Existing transcriptomic methods often miss compact gene sets corresponding to subprocesses in specialized cell types, limiting insights into functional heterogeneity. RESULTS: We present Nested-WGCNA, a two-stage unsupervised network analysis algorithm designed to identify coarse-grained and fine-grained gene modules. Applied to bulk RNA-Seq data, Nested-WGCNA reveals stable modules reproducible across datasets. When validated against scRNA-Seq data, these modules correspond to both major and minor immune cell subtypes. Application to immunotherapy response datasets uncovers predictive and prognostic biomarkers, highlighting its utility in treatment stratification and biomarker discovery. AVAILABILITY: The NestedWGCNA source code and analysis pipeline are available on GitHub (https://github.com/ilyada/NestedWGCNA) and archived on Zenodo (https://doi.org/10.5281/zenodo.18959244).

Algorithms↗

PubMed1992

Network analysis of Korean health insurance policy-making process.

This study examines how the decision-making process evolved in Korea during the initial phases of introduction and implementation of National Health Insurance. This study analyses the official documents and interviews views made with government officials and related personnel. We used the method of network analysis and multidimensional scaling in order to demonstrate how the major participants in the decision-making process developed and changed under the contemporary political situations. In the pre-implementation stage around 1976, major concerns were concentrated around the issues of financial support for social insurance, the fee schedule and who ought to be covered first. The total number of participants of the health or health-related organization was 61, which included the President, the Minister of Health and Social Affairs, representatives of special interest groups, etc. In the actual implementation period of 1982, different issues were brought up by the major participants. The number of participants in this period declined to 44 with the deletion of 19 and with the addition of two newly formed health insurance organizations. By 1988, as the implementation reached its final decision period, disagreements were centered on progressive premium rating and the administration of National Health Insurance. The number of participants increased to 60 after the addition of 16 participants. The analysis of this paper may provide some insight for other countries which wish to establish National Health Insurance; as reference to the policy-making process, it may provide some suggestions for when to initiate and how to formulate National Health Insurance policies.

Humans↗

PubMed1993

Artificial neural network analysis of pyrolysis mass spectrometric data in the identification of Streptomyces strains.

Sixteen representatives of three morphologically distinct groups of streptomycetes were recovered from soil using selective isolation procedures. Duplicated batches of the test strains were examined by Curie-point pyrolysis mass spectrometry and the first data set used for conventional multivariate statistical analyses and as a training set for an artificial neural network. The second set of data was used for 'operational fingerprinting' and for testing the artificial neural network. All of the test strains were correctly identified using the artificial neural network whereas only fifteen of the sixteen strains were assigned to the correct group using the conventional operational fingerprinting procedure. Artificial neural network analysis of pyrolysis mass spectrometric data provides a rapid, cost-effective and reproducible way of identifying and typing large numbers of microorganisms.

Bacterial Typing Techniques↗

PubMed2006

Application of a quasi-median network analysis for the visualization of character conflicts to a population sample of mitochondrial DNA control region sequences from southern Germany (Ulm).

Entire mtDNA control region sequences from 100 individuals in a west Eurasian population sample from southern Germany (around the city of Ulm) were generated and analyzed. The control region was amplified in one piece and sequenced with ten different sequencing primers. Sequence evaluation was performed independently. Phylogenetic analyses were used for quality assurance purposes and for the determination of the haplogroup affiliation of the samples. The sequences were scrutinized performing a quasi-median network analysis. To visualize character conflicts, frequent mutations were filtered, and the reduced data were represented by the torso of their quasi-median network. Character incompatibilities were found to be based on real biological patterns of homoplasy. The population data will be incorporated in the EMPOP database ( http://www.empop.org ).

DNA, Mitochondrial↗

PubMed1991

Use of neural network analysis to classify electroencephalographic patterns against depth of midazolam sedation in intensive care unit patients.

The electroencephalographic (EEG) analog signal is complex and cannot easily be described by univariate variables. Clear visual changes in the EEG power spectrum can be present with little or no change in univariate variable values. A method that could produce a single value based on the total data available in the EEG power spectrum would be very useful in monitoring EEG changes. Neural network analysis is a technique that can take multiple inputs and produce a single output value using complicated processing patterns that require training to establish. We examined the usefulness of a series of neural network models to classify 63 EEG patterns against sedation level in 26 mechanically ventilated patients requiring midazolam for long-term sedation. During a stable period of sedation, a 4- to 60-minute period of EEG data was obtained concurrently with a sedation level from 1 (follows commands) to 7 (no or gag response to suctioning of the endotracheal tube). The EEG power spectrum was divided into equal frequency bands, and the log absolute powers in each of these bands were used as inputs for a series of neural network models. The output target was the sedation level associated with each set of EEG data. Networks were trained on a subset of EEG power/sedation score data pairs, and the ability to classify the remaining data pairs was tested. Using a t-test comparison with a random set of sedation levels, we found that trained neural network models classified EEG patterns against sedation level successfully (p less than 0.001).(ABSTRACT TRUNCATED AT 250 WORDS)

Adolescent↗