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PubMed2006

Extraction of phylogenetic network modules from the metabolic network.

BACKGROUND: In bio-systems, genes, proteins and compounds are related to each other, thus forming complex networks. Although each organism has its individual network, some organisms contain common sub-networks based on function. Given a certain sub-network, the distribution of organisms common to it represents the diversity of its function. RESULTS: We extracted such "common" sub-networks, defined as "phylogenetic network modules," using phylogenetic profiles and cluster analysis. The enzymes in the same "phylogenetic network module" have similar phylogenetic profiles and related functions. These modules are shown to be phylogenetic building blocks. Furthermore, the network of the modules illustrated hierarchical feature as well as the network of enzymes involved in the metabolism. CONCLUSION: We conclude that phylogenetic network modules are evolutionary conserved functional units in the metabolic network. We claim that our concept of phylogenetic modules provides a more accurate understanding of the evolution of biological networks.

Animals↗

PubMed2004

Interorganizational relationships within state tobacco control networks: a social network analysis.

INTRODUCTION: State tobacco control programs are implemented by networks of public and private agencies with a common goal to reduce tobacco use. The degree of a program's comprehensiveness depends on the scope of its activities and the variety of agencies involved in the network. Structural aspects of these networks could help describe the process of implementing a state's tobacco control program, but have not yet been examined. METHODS: Social network analysis was used to examine the structure of five state tobacco control networks. Semi-structured interviews with key agencies collected quantitative and qualitative data on frequency of contact among network partners, money flow, relationship productivity, level of network effectiveness, and methods for improvement. RESULTS: Most states had hierarchical communication structures in which partner agencies had frequent contact with one or two central agencies. Lead agencies had the highest control over network communication. Networks with denser communication structures had denser productivity structures. Lead agencies had the highest financial influence within the networks, while statewide coalitions were financially influenced by others. Lead agencies had highly productive relationships with others, while agencies with narrow roles had fewer productive relationships. Statewide coalitions that received Robert Wood Johnson Foundation funding had more highly productive relationships than coalitions that did not receive the funding. CONCLUSION: Results suggest that frequent communication among network partners is related to more highly productive relationships. Results also highlight the importance of lead agencies and statewide coalitions in implementing a comprehensive state tobacco control program. Network analysis could be useful in developing process indicators for state tobacco control programs.

Academies and Institutes↗

PubMed2006

Modeling of workflow-engaged networks on radiology transfers across a metro network.

Radiology metro networks bear the challenging proposition of interconnecting several hospitals in a region to provide a comprehensive diagnostic imaging service. Consequences of a poorly designed and implemented metro network could cause delays or no access at all when health care providers try to retrieve medical cases across the network. This could translate into limited diagnostic services to patients, resulting in negative impacts to the patients' medical treatment. A workflow-engaged network (WEN) is a new network paradigm. A WEN appreciates radiology workflows and priorities in using the network. A WEN greatly improves the network performance by guaranteeing that critical image transfers experience minimal delay. It adjusts network settings to ensure the application's requirements are met. This means that high-priority image transfers will have guaranteed and known delay times, whereas lower-priority traffic will have increased delays. This paper introduces a modeling to understand the benefits that WEN brings to a radiology metro network. The modeling uses actual data patterns and flows found in a hospital metro region. The workflows considered are based on the Integrating the Healthcare Enterprise profiles. This modeling has been applied to metropolitan workflows of a health region. The modeling helps identify the kind of metro network that supports data patterns and flows in a metro area. The results of the modeling show that a 155-Mb/s metropolitan area network (MAN) with WEN operates virtually equal to a normal 622-Mb/s MAN without WEN, with potential cost savings for leased line services measured in the millions of dollars per year.

Canada↗

PubMed1996

Multiple neural network response variability as a predictor of neural network accuracy for chromosome recognition.

Human chromosome classification requires all chromosome appearing in a microphotograph of a dividing human cell to be classified within the known normal or abnormal 24 chromosome types. In recent years, research has focused on the use of neural networks for classification of normal chromosomes. Experimental work in this area led us to question whether learning variability, resulting when multiple neural networks are trained to solve the same problem, could be used as a predictor of classification performance. The Copenhagen chromosome data bank, consisting of 30-component feature vectors from 8106 chromosomes isolated from 180 cells, was divided into a training and a test subsets. Back propagation neural networks with 30 input nodes, 1 to 100 nodes in the hidden layer, and 24 output nodes were trained with the same learning parameters. After training, each neural network was tested. The neural network yielding the best classification was labeled as the optimal neural network. An error variability score was calculated for each test chromosome. This score was a function of all (100) neural network outputs obtained for that chromosome. The error variability scores ranged from 0.16 to 1.31 with a mean value of 0.41 and a SD of 0.12. There was significant difference (p < 0.0001) between the variability scores from chromosomes classified correctly (mean = 0.4, SD = 0.1, n = 3804) and incorrectly (mean = 0.62, SD = 0.19, n = 241) by the optimal neural network. When the variability score was used as a threshold to decide whether or not to accept the output of the optimal neural network, a peak classification rate of 98.93% was observed for chromosomes with an error variability score < 0.35. Results indicate that the error variability of multiple neural network responses can be used as a confidence indicator for a optimal neural network.

Humans↗

PubMed2006

[The German competence network inflammatory bowel disease (KNCED) -- network research leads to the identification of the cause of disease and to the improvement in patient care].

The competence network chronic inflammatory bowel disease (KN-CED) is one of 17 networks of competence initiated by the German Federal Ministry of Education and Research (BMBF). These networks are concerned with disease patterns which are characterized by their high frequency, high mortality rate or which present a large expense factor. The project-executing organization is the German Center for Air and Space Travel (DLR e. V.). The central structure of organization is the Telematic Platform for medical Networks (TMF e. V.). Aim of the KN-CED is to investigate, in their complexity, the incurable chronic diseases ulcerative colitis and Crohn's disease, particularly with regard to the causes of disease, the establishment of new therapy standards as well as patient care. To achieve this goal, the competence network is integrated into both national and international research associations and is also backed by the national self-help group DCCV and the pharmaceutical industry. Principal items of the competence network are the core facilities and their main focus on molecular genetics, animal and cell models and serum markers. Having stored the data of more than 4,000 patients so far, the central database of the competence network is one of the largest databases worldwide with regard to inflammatory bowel disease (IBD). The successful cooperation within the network is reflected in numerous publications. Thus, two of the three known genes of Crohn's disease were identified. Also with the participation of the competence network national guidelines for the diagnosis and therapy of IBD were generated.Furthermore, the competence network operates study centers where significant therapeutic developments in the field of biotechnological drugs are taking place. The analysis of existing structures of care as well as the development of standards of organization for patients with IBD top the research within the competence network and emphasize the claim to find comprehensive answers to the questions connected with IBD.

Clinical Trials as Topic↗

PubMed2004

Topological analysis of mass-balanced signaling networks: a framework to obtain network properties including crosstalk.

Signal transduction networks have only been studied at a small scale because large-scale reconstructions and suitable in silico analysis methods have not been available. Since reconstructions of large signaling networks are progressing well there is now a need to develop a framework for analysing structural properties of signaling networks. One such framework is presented here, one that is based on systemically independent pathways and a mass-balanced representation of signaling events. This approach was applied to a prototypic signaling network and it allowed for: (1) a systemic analysis of all possible input/output relationships, (2) a quantitative evaluation of network crosstalk, or the interconnectivity of systemically independent pathways, (3) a measure of the redundancy in the signaling network, (4) the participation of reactions in signaling pathways, and (5) the calculation of correlated reaction sets. These properties emerge from network structure and can only be derived and studied within a defined mathematical framework. The calculations presented are the first of their kind for a signaling network, while similar analysis has been extensively performed for prototypic and genome-scale metabolic networks. This approach does not yet account for dynamic concentration profiles. Due to the scalability of the stoichiometric formalism used, the results presented for the prototypic signaling network can be obtained for large signaling networks once their reconstruction is completed.

Animals↗

PubMed1998

The neural network approach to a parallel decentralized network routing.

With the progress of high-speed optical transmission and packet switching, a large capacity packet-based multi-media communication network is expected to spread rapidly. One of the key issues in these networks is the network routing that chooses the route to the destination for packet transmission in the network. In most previous work, the whole network is mapped to a large size-Hopfield-type neural network. Hence, the network routing by this method is not beyond the centralized control. In this paper, a parallel decentralized Network Routing method is presented. The model comprises an interconnection of groups of an intraconnected network, which is fully connected, and resides at each node of the communication network. Since the dynamics of each neuron in the whole system follows a unique state equation, we can see easily how the update of a neuron maps to real world network routing problems. Most important, becauase of the dynamics of the neurons with such a high speed of convergence, the model has the ability to achieve a sub-optimum routing solution in a real-time application. Finally, simulation results validate the proposed method.

Journal Article↗

PubMed2005

International School Health Network: an informal network for advocacy and knowledge exchange.

In Canada, researchers, policy-makers and non-governmental organisations have re-conceptualized the school setting as being an ecological entity, linked to parallel ecologies of the homes and the community it serves. The school, public health and other systems that seek to deliver programs in that setting are open, loosely coupled and bureaucratic. This reconceived view of the school as a setting for health promotion leads to an emphasis on building organizational, system, professional and community capacity. One of the most effective ways of building such capacities when resources are scarce is to invest in a variety of formal and informal networks that can sustain themselves with little or no external resources. A number of recognised researchers from the health and education sectors have emphasized this systems-based approach and the need to build supportive, small-scale networks or learning communities. In recent health promotion research, networking at various levels, across sectors and within communities is viewed as a key strategy within new, more effective health promotion strategies. In education, the notion of networking for educational change has been described as "learning communities" for continuous school improvement. The authors suggest that this strategy of networking be used at the international level to address several global challenges: There is no single, convenient way to obtain basic information about the status and nature of national and state/provincial school health programs around the world. There is no global research agenda in school health promotion, despite the obvious value of sharing such research and knowledge. There is no global mechanism to facilitate the development of common or shared tools for surveillance of child/youth health and monitoring of school health policies and programs, despite the excellent work being done in individual countries and by the European Network of Health Promoting Schools. There is no international mechanism with a focus on school health that brings together the following stakeholders: educational organisations with public health organisations; researchers with government officials and practitioners; those who work in Spanish, English, French and other languages; issue-specific networks with health generalists. An invitation is given to government officials, researchers and national school health associations to join an informal International School Health Network (ISHN) (soon available at: www.internationalschoolhealt.org). Discussions about the formation of the ISHN have been held with a number of participants at several international meetings and have culminated in a fledgling network that will focus on electronic and web-based exchanges of information, developing a global school health research agenda, exchanging effective materials and tools, informing policy-makers about effective practices, policies and programs. This network would build on and not duplicate the work of existing networks and include participation from WHO, OECD, UNESCO, the IUHPE and the World Bank. The next large meeting of the ISHN will occur at the IUHPE 2007 Conference in Vancouver, Canada (www.iuhpe conference.org). Before then the ISHN will organise several on-line projects and teleconferences. For more information, contact dmccall@jcsh-cces.ca.

Communication↗

PubMed2006

Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence.

Topological measures of large-scale complex networks are applied to a specific artificial regulatory network model created through a whole genome duplication and divergence mechanism. This class of networks share topological features with natural transcriptional regulatory networks. Specifically, these networks display scale-free and small-world topology and possess subgraph distributions similar to those of natural networks. Thus, the topologies inherent in natural networks may be in part due to their method of creation rather than being exclusively shaped by subsequent evolution under selection. The evolvability of the dynamics of these networks is also examined by evolving networks in simulation to obtain three simple types of output dynamics. The networks obtained from this process show a wide variety of topologies and numbers of genes indicating that it is relatively easy to evolve these classes of dynamics in this model.

Computational Biology↗

PubMed2002

The cystic fibrosis therapeutics development network (CF TDN): a paradigm of a clinical trials network for genetic and orphan diseases.

Clinical trials have become critical to the advancement of medical science and to the evolution of patient care in medicine. The science of clinical research has advanced from early studies in which treatment was assessed without controls to sophisticated multinational collaborative randomized, double-blind, placebo controlled trials of therapeutic interventions. To facilitate the advancement of clinical research, clinical trials networks have been developed to conduct multicenter studies. This review describes the history of clinical trials, clinical trials networks, and the goals of such networks in the United States. The Cystic Fibrosis Therapeutics Development Network, a network that represents the paradigm for genetic and orphan diseases, is described in detail. This network has been extremely successful in its first 3.5 years of existence conducting 18 different clinical trials in patients with Cystic Fibrosis. Unique aspects of the network include the use of internet applications for study conduct and communication, the development of statistical methodology to enhance the efficiency of clinical trial design, the development of outcome measures specific to Cystic Fibrosis, and the development of infrastructure necessary for expediting protocol development. In the current environment, clinical research faces significant challenges related to ensuring the safe and ethical conduct of clinical research while promoting fast and efficient clinical trials. To succeed and move forward to provide treatments and find cures for diseases, clinical trials networks must continue to evolve. The Cystic Fibrosis Therapeutics Development Network represents a network that has met this challenge and will continue to provide a venue for the safe and efficient conduct of clinical trials in Cystic Fibrosis.

Clinical Trials as Topic↗

PubMed1991

Designing a neural network simulator--the MENS modelling environment for network systems: I.

During recent years, the field of neural network research has increasingly attracted the interest of workers from a large number of different disciplines. Current research topics include aspects as different as detailed simulations in brain physiology, predictions of protein structure in biochemistry, database organization in computer science, or various technical applications. The common scheme behind these different approaches is the use of distributed networks of simple computational elements that communicate with each other by means of weighted links. Computer simulations of neural networks require an appropriate software environment. Due to the computational similarities of many classes of such networks, simulation software can be structured into modular components that, to a large degree, are independent of specific applications. The aim of this and the following paper is to discuss some of the design considerations concerning software for neural network simulations. The aspects presented are interesting for both the development of new simulation software and the efficient use and modification of existing programs. Therefore, the general user as well as the software designer may hopefully benefit from this material. This paper briefly introduces some of the basic principles of neural networks. After a short discussion of different approaches to software design, two simple example applications are presented in order to demonstrate a conceptual framework common to many network simulations. The transfer of these considerations to the design of simulation software is then shown by example of the MENS network simulator developed in the Max-Planck-Institute for Brain Research. The paper gives a general introduction to the layout of data structures and different software components. Using the two introductory examples some aspects of network analysis are demonstrated. The following paper then considers further details of the design of a neural network simulator with respect to performance, implementation, and testing.

Animals↗

PubMed2025

Gene behaviors-based network enrichment analysis and its application to reveal immune disease pathways enriched with COVID-19 severity-specific gene networks.

MOTIVATION: Gene network analysis is essential for understanding the complex mechanisms underlying diseases, which often involve disruptions in molecular networks rather than individual genes. Despite the availability of large-scale omics datasets and computational tools for gene network analysis, interpretation of the biological relevance of these extensive networks remains challenging. RESULTS: We propose a novel computational strategy, gene behaviors-based network enrichment analysis, which systematically identifies functional pathways enriched in phenotype-specific gene networks. Our novel method incorporates comprehensive network characteristics, i.e. gene expression levels, edge strengths, and structural patterns of edges, to rank genes based on activity and assess pathway enrichment, effectively identifying functional pathways enriched within these networks. Through simulation studies, our strategy demonstrated superior performance compared with that of existing methods in identifying enriched pathways. We applied this strategy to whole-blood RNA-seq data from 1102 COVID-19 samples provided by the Japan COVID-19 Task Force. The analysis revealed immune disease pathways enriched with COVID-19 severity-specific gene networks, including "Systemic lupus erythematosus" in asymptomatic and severe samples and "Inflammatory bowel disease," "Primary immunodeficiency," and "Rheumatoid arthritis" in mild samples. Key biomarkers of COVID-19, such as CXCL8, S100A9, and HLA class I genes, have been identified as critical hub genes and the main players within these networks. AVAILABILITY AND IMPLEMENTATION: Code is available in Figshare (https://doi.org/10.6084/m9.figshare.29093648.v3).

COVID-19↗

PubMed2002

Prediction models in the design of neural network based ECG classifiers: a neural network and genetic programming approach.

BACKGROUND: Classification of the electrocardiogram using Neural Networks has become a widely used method in recent years. The efficiency of these classifiers depends upon a number of factors including network training. Unfortunately, there is a shortage of evidence available to enable specific design choices to be made and as a consequence, many designs are made on the basis of trial and error. In this study we develop prediction models to indicate the point at which training should stop for Neural Network based Electrocardiogram classifiers in order to ensure maximum generalisation. METHODS: Two prediction models have been presented; one based on Neural Networks and the other on Genetic Programming. The inputs to the models were 5 variable training parameters and the output indicated the point at which training should stop. Training and testing of the models was based on the results from 44 previously developed bi-group Neural Network classifiers, discriminating between Anterior Myocardial Infarction and normal patients. RESULTS: Our results show that both approaches provide close fits to the training data; p = 0.627 and p = 0.304 for the Neural Network and Genetic Programming methods respectively. For unseen data, the Neural Network exhibited no significant differences between actual and predicted outputs (p = 0.306) while the Genetic Programming method showed a marginally significant difference (p = 0.047). CONCLUSIONS: The approaches provide reverse engineering solutions to the development of Neural Network based Electrocardiogram classifiers. That is given the network design and architecture, an indication can be given as to when training should stop to obtain maximum network generalisation.

Electrocardiography↗

PubMed2006

Connectivity in the yeast cell cycle transcription network: inferences from neural networks.

A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN) models can effectively discover gene network structure by integrating global in vivo protein:DNA interaction data (ChIP/Array) with genome-wide microarray RNA data. We test this on the yeast cell cycle transcription network, which is composed of several hundred genes with phase-specific RNA outputs. These ANNs were robust to noise in data and to a variety of perturbations. They reliably identified and ranked 10 of 12 known major cell cycle factors at the top of a set of 204, based on a sum-of-squared weights metric. Comparative analysis of motif occurrences among multiple yeast species independently confirmed relationships inferred from ANN weights analysis. ANN models can capitalize on properties of biological gene networks that other kinds of models do not. ANNs naturally take advantage of patterns of absence, as well as presence, of factor binding associated with specific expression output; they are easily subjected to in silico "mutation" to uncover biological redundancies; and they can use the full range of factor binding values. A prominent feature of cell cycle ANNs suggested an analogous property might exist in the biological network. This postulated that "network-local discrimination" occurs when regulatory connections (here between MBF and target genes) are explicitly disfavored in one network module (G2), relative to others and to the class of genes outside the mitotic network. If correct, this predicts that MBF motifs will be significantly depleted from the discriminated class and that the discrimination will persist through evolution. Analysis of distantly related Schizosaccharomyces pombe confirmed this, suggesting that network-local discrimination is real and complements well-known enrichment of MBF sites in G1 class genes.

Artificial Intelligence↗

PubMed2004

Networking genetic regulation and neural computation: directed network topology and its effect on the dynamics.

Two different types of directed networks are investigated, transcriptional regulation networks and neural networks. The directed network structure is studied and is also shown to reflect the different processes taking place on the networks. The distribution of influence, identified as the the number of downstream vertices, are used as a tool for investigating random vertex removal. In the transcriptional regulation networks we observe that only a small number of vertices have a large influence. The small influences of most vertices limit the effect of a random removal to, in most cases, only a small fraction of vertices in the network. The neural network has a rather different topology with respect to the influence, which are large for most vertices. To further investigate the effect of vertex removal we simulate the biological processes taking place on the networks. Opposed to the presumed large effect of random vertex removal in the neural network, the high density of edges in conjunction with the dynamics used makes the change in the state of the system to be highly localized around the removed vertex.

Algorithms↗

PubMed2005

Scale-free user-network approach to telephone network traffic analysis.

The effect of the user network on the telephone network traffic is studied in this paper. Unlike classical traffic analysis, where users are assumed to be connected uniformly, our proposed method employs a scale-free network to model the behavior of telephone users. Each user has a fixed set of acquaintances with whom the user may communicate, and the number of acquaintances follows a power-law distribution. We show that compared to conventional analysis based upon a fully connected user network, the network traffic is significantly different when the user network assumes a scale-free property. Specifically, network blocking (call failure) is generally more severe in the case of a scale-free user network. It is also shown that the carried traffic is practically limited by the scale-free property of the user network, rather than by the network capacity.

Journal Article↗

PubMed2026

BioNeuralNet: a graph neural network based Multi-Omics network data analysis tool.

SUMMARY: Multi-omics data offer unprecedented insights into complex biological systems, yet their high dimensionality, sparsity, and intricate interactions pose significant analytical challenges. Network-based approaches have advanced multi-omics research by effectively capturing biologically relevant relationships among molecular features (e.g., genes, proteins, metabolites). While these methods are powerful for representing molecular interactions, there remains a need for tools specifically designed to effectively utilize these network representations across diverse downstream analyses. To fulfill this need, we introduce BioNeuralNet, a flexible and modular Python framework tailored for end-to-end network-based multi-omics data analysis. BioNeuralNet leverages Graph Neural Networks (GNNs) to learn biologically meaningful low-dimensional representations from multi-omics networks, converting these complex molecular networks into versatile embeddings. BioNeuralNet supports all major stages of multi-omics network analysis, including several network construction techniques, generation of low-dimensional representations, and a broad range of downstream analytical tasks. Its extensive utilities, including diverse GNN architectures, and compatibility with established Python packages (e.g., scikit-learn, PyTorch, NetworkX), enhance usability and facilitate quick adoption. BioNeuralNet is an open-source, user-friendly, and extensively documented framework designed to support flexible and reproducible multi-omics network analysis in precision medicine. AVAILABILITY AND IMPLEMENTATION: The BioNeuralNet library is available via The Python Package Index (PyPI). Source code, documentation, tutorials, and workflows are hosted at https://bioneuralnet.readthedocs.io. Code archived at https://doi.org/10.5281/zenodo.17503083.

Graph Neural Networks↗

PubMed2002

Interorganizational networks: using a theoretical model to predict effectiveness of rural health care delivery networks.

Interorganizational health care delivery networks have potential for sustaining health services delivery in rural areas faced with economic and demographic challenges. Four Nebraska rural health care delivery networks (Albion-Ord, Blue River Valley, Rural Partners, Inc., and Western Nebraska) were compared to an interorganizational model based on theories of interorganizational relations, exchange, population ecology, and synthesized collaboration. It assumes that outcomes, including effectiveness, are influenced by external and internal factors that are operationalized through external control, technology, structure, and operational process variables. Data were collected by a non-random, two-level cluster mail survey of network members (45/59 = 76.3% response rate). All networks received technical assistance from the Nebraska Office of Rural Health. Networks have formal organization, strategic plans, and official coordinators. Hospital administrators hold most leadership positions; few doctors or citizens are involved. Correlation and multiple regression analysis show partial fit between the research model and study networks. Effectiveness, measure by the gap between best possible and actual practice, increased with network connectivity (r=.36, p<.05), group methods of administrative decision-making (r=.52, p<.001) and sequential pattern of service delivery (r=.39, p<.05). Greater dependence on vertical funding corresponds to greater external control (r=.43, p<.01). The prediction that, as scope narrows, task intensity (r=.56, p<.001), duration (r=.41, p<.01), and task volume (r=.50, p<.01) increase is upheld. Centrality and network size decrease together (r=.43, p<.01) where there is little reliance on vertical sources of funds (r=.36, p<.05). The integrated interorganizational model demonstrates some efficacy for testing potential effectiveness of networks.

Community Networks↗