PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Networks”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources
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↗
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↗
PubMedDate not supplied

Organization of network properties of cells in local and distributed neuronal networks of the brain of cats.

The network properties of neurons of the visual and motor cortex and of the lateral nucleus of the hypothalamus were investigated on the basis of identified interneuronal interactions, using the cross-correlation method of analysis, in cats with developed alimentary conditioned instrumental reflexes to light. The varied organization of the network properties of cortical neurons in the organization of local and distributed neuronal networks was demonstrated, namely: the predominance of divergent properties over convergent properties for large cells in local networks and the leveling out of these relationships in distributed networks. The neurons of the lateral nucleus of the hypothalamus had an equal representation of convergent and divergent properties in the organization of local and distributed networks. The network properties of neurons of the cortical and subcortical structures were manifested in the background, following the development of conditioned reflexes, and during extinction. Only the small cells of the visual cortex were functionally dependent and changed the relationship of network properties in local networks during the extinction of conditioned reflexes.

Action Potentials↗
PubMed2025

MiNEApy: enhancing enrichment network analysis in metabolic networks.

MOTIVATION: Modeling genome-scale metabolic networks (GEMs) helps understand metabolic fluxes in cells at a specific state under defined environmental conditions or perturbations. Elementary flux modes (EFMs) are powerful tools for simplifying complex metabolic networks into smaller, more manageable pathways. However, the enumeration of all EFMs, especially within GEMs, poses significant challenges due to computational complexity. Additionally, traditional EFM approaches often fail to capture essential aspects of metabolism, such as co-factor balancing and by-product generation. The previously developed Minimum Network Enrichment Analysis (MiNEA) method addresses these limitations by enumerating alternative minimal networks for given biomass building blocks and metabolic tasks. MiNEA facilitates a deeper understanding of metabolic task flexibility and context-specific metabolic routes by integrating condition-specific transcriptomics, proteomics, and metabolomics data. This approach offers significant improvements in the analysis of metabolic pathways, providing more comprehensive insights into cellular metabolism. RESULTS: Here, I present MiNEApy, a Python package reimplementation of MiNEA, which computes minimal networks and performs enrichment analysis. I demonstrate the application of MiNEApy on both a small-scale and a genome-scale model of the bacterium Escherichia coli, showcasing its ability to conduct minimal network enrichment analysis using minimal networks and context-specific data. AVAILABILITY AND IMPLEMENTATION: MiNEApy can be accessed at: https://github.com/vpandey-om/mineapy.

Metabolic Networks and Pathways↗
PubMed1992

An overview of networking for physicians and problems of network consulting in remote areas.

For computer networking the most suitable operating systems are UNIX or MS-DOS. As networking software UUCP and TCP/IP are most common. Hardware requirements are derived from the operating system and from the networking software. Low-cost solutions, for example, uuPC, a public domain version of UUCP, require only an 8088 processor and a 2400-baud modem. TCP/IP fares better with more powerful processors and requires permanent lines between the connecting computers. In developing countries the introduction of computer networks is hampered by several factors: lack of foreign exchange, price of hardware and software, unreliable electricity and telephone lines, lack of hardware and software support, large distances to the nearest center, and incompatibilities between existing systems and the network. Important aspects for clinical networking in developing countries include appointment scheduling in the referral hospitals, access to laboratory and pathology results from the central laboratory, and primary health care information such as epidemiologic data. Advanced systems, for example, for image processing, are not yet feasible in developing countries.

Computer Communication Networks↗
PubMed1992

A computer-based model for realistic simulations of neural networks. II. The segmental network generating locomotor rhythmicity in the lamprey.

1. To analyze the function of the spinal interneuronal network generating locomotion in the lamprey CNS, a vertebrate model system, we performed computer simulations with realistic model neurons possessing the essential properties of their biological counterparts. 2. The segmental network has been simulated by modeling experimentally established types of neurons with their specific membrane properties and synaptic interconnections. Fictive locomotor activity, which can be experimentally induced by elevating the background excitability by bath application of excitatory amino acids, was simulated by opening membrane conductances for kainate/alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) or N-methyl-D-aspartate (NMDA) receptors. Kainate/AMPA receptor activation induced a rhythm in the middle and upper part of the physiological burst frequency range, whereas NMDA receptor activation evoked bursting in the lower part of the range, which corresponds well to earlier experimental findings. 3. Several factors contributing to the termination of the burst were studied and their interaction was assessed in simulations of the network. 1) The summation of spike afterhyperpolarizations (late AHPs), leading to adaptation of the discharge, acts as a primary burst-terminating factor at lower rates of kainate/AMPA-induced bursting, and it also interacts with the NMDA-induced oscillatory membrane properties during slow rhythmicity. 2) The termination of the depolarized NMDA plateau is another important factor during NMDA-evoked rhythmicity. 3) The synaptic inhibition from lateral interneurons to the interneurons mediating reciprocal inhibition is important at higher rates of kainate/AMPA-induced bursting. 4. The mechanism of action of 5-hydroxytryptamine (5-HT) on the lamprey segmental network was further investigated by simulation. 5-HT is known to lower the burst frequency during fictive locomotion and also to decrease the conductance through the Ca(2+)-dependent K+ channels, and thereby the size of the late AHP that follows the action potential. Decreasing this conductance in the network simulations resulted in a lesser amount of AHP summation and thereby less frequency adaptation during the burst, longer bursts, and a lower locomotor frequency. Thus the selective action of 5-HT on the Ca(2+)-dependent K+ channels, and hence on the AHP, can account for the modulatory effect on the fictive locomotor rhythm seen experimentally. 5. The results demonstrate that the present simulation of the segmental network can account for essential features of the motor pattern seen experimentally during lamprey locomotion.(ABSTRACT TRUNCATED AT 400 WORDS)

Animals↗
PubMed1991

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

During recent years, neural network research has been extended to a large number of different fields, increasingly attracting the interest of workers from various disciplines. The computer simulations carried out with this research require an appropriate software environment. The computational similarities of many kinds of simulations allow the design of software components that are largely independent of the specific application. These considerations are reflected, for example, by the general layout of the MENS network simulator, as described in the accompanying first paper. This paper presents the design considerations for the simulator's different software components in more detail. In particular, design and implementation are discussed with respect to computational and memory efficiency. The discussion includes, for example, the representation of a network by the simulator's data structure, the file-driven configuration and initialization of a network, the simulator's stimulus and monitor system, and the simulator's control structures. In addition, the separation and interaction of application-specific and application-independent software components are addressed. Particular performance aspects comprise the implementation of synaptic delays, the dynamic deletion of synaptic links in network learning, and the preprocessing of stimulus films. In addition, some general aspects of simulator performance and testing are considered. The material presented in this paper concerns both the development of new simulation software and the efficient use of existing programs. Therefore, both the general user as well as the software designer may hopefully benefit from this presentation.

Animals↗
PubMed1992

The use of wide area computer networks in disaster management and the implications for hospital/medical networks.

Computer-mediated communication in various forms is already being used in all phases of disaster management--preparation, response, recovery, and long-term mitigation. However, to date wide area computer networks--particularly the Internet (the supernetwork of networks)--have been used only to a limited extent in disaster management and prevention. Some of these applications are described in this paper. Nevertheless, the high speed and ease of information transfer by computer network and the vast resources becoming available on the Internet make it inevitable that the use of computer networks to temper disasters will increase enormously in the next decade. The Internet will provide a key means through which networks initially dedicated to solely medical purposes and the individuals who use them will become involved not only in disaster response and mitigation worldwide, but in the global community and consciousness that is the Internet.

Australia↗
PubMed2026

Integrative computational analysis combining network pharmacology, regulatory network modeling, and molecular dynamics reveals the mechanisms of Quanshen compound in ITP.

UNLABELLED: Immune thrombocytopenia (ITP) is a hemorrhagic disorder caused by immune dysfunction. Quanshen Compound (QSC) is an in-house preparation developed by the Uyghur Hospital in Hotan Prefecture. This study primarily investigates and validates the potential pharmacological basis and mechanism of action of QSC in modulating immune thrombopoiesis. Based on the multi-database screening of the QSC and the related targets of ITP, the intersection was obtained to construct a protein-protein interaction (PPI) network and screen the core targets; the intersection targets were analyzed for gene ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using R packages; a component-target-pathway network was constructed to screen the key active components and their mechanisms of action. At the same time, the TF-mRNA-miRNA regulatory network of the core targets was constructed, and chromosome localization and subcellular localization analysis were performed; further, the binding stability of key components and core targets was verified through molecular docking and molecular dynamics simulation. A total of 227 potential target sites were screened out, among which TNF, IL6, AKT1, TP53 and IL1B were the core targets. The enrichment results indicated that these intersecting target sites mainly participated in inflammatory responses, immune regulation and hemostasis-related biological processes, and were significantly enriched in the PI3K-Akt signaling pathway, Toll-like receptor signaling pathway, Th17 cell differentiation and PD-1/PD-L1 signaling pathway. The core target TF-mRNA-miRNA regulatory network contained 184 nodes and 200 edges, suggesting that the core targets were subject to multi-level regulation. Molecular docking results showed that the main active components had good binding activity with the core targets, and molecular dynamics simulation further verified the stability of the complex. QSC may improve ITP through a multi-component, multi-target, and multi-pathway synergistic mechanism involving key targets such as TNF, IL6, AKT1, TP53, and IL1B, as well as the PI3K-Akt signaling pathway. These findings provide new insights into the potential therapeutic mechanisms of QSC against ITP and warrant further experimental validation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s40203-026-00718-0.

Immune thrombocytopenia↗
PubMed2025

Integration of multi-source gene interaction networks and omics data with graph attention networks to identify novel disease genes.

MOTIVATION: The pathogenesis of diseases is closely associated with genes, and the discovery of disease genes holds significant importance for understanding disease mechanisms and designing targeted therapeutics. However, biological validation of all genes for diseases is expensive and challenging. RESULTS: In this study, we propose DGP-AMIO, a computational method based on graph attention networks, to rank all unknown genes and identify potential novel disease genes by integrating multi-omics and gene interaction networks from multiple data sources. DGP-AMIO outperforms other methods significantly on 20 disease datasets, with an average AUROC and AUPR exceeding 0.9. The superior performance of DGP-AMIO is attributed to the integration of multiomics and gene interaction networks from multiple databases, as well as triGAT, a proposed GAT-based method that enables precise identification of disease genes in directed gene networks. Enrichment analysis conducted on the top 100 genes predicted by DGP-AMIO and literature research revealed that a majority of enriched GO terms, KEGG pathways and top genes were associated with diseases supported by relevant studies. We believe that our method can serve as an effective tool for identifying disease genes and guiding subsequent experimental validation efforts. AVAILABILITY AND IMPLEMENTATION: DGP-AMIO is publicly available at https://github.com/yangkaiyuan1027/DGP-AMIO.

Gene Regulatory Networks↗
PubMedDate not supplied

3-D components of a biological neural network visualized in computer generated imagery. II. Macular neural network organization.

Computer-assisted reconstructions of small parts of the macular neural network show how the nerve terminals and receptive fields are organized in 3-dimensional space. This biological neural network is anatomically organized for parallel distributed processing of information. Processing appears to be more complex than in computer-based neural networks, because spatiotemporal factors figure into synaptic weighting. Serial reconstruction data show anatomical arrangements which suggest that 1) assemblies of cells analyse and distribute information with inbuilt redundancy, to improve reliability; 2) feedforward/feedback loops provide the capacity for presynaptic modulation of output during processing; 3) constrained randomness in connectivities contributes to adaptability; and 4) local variations in network complexity permit differing analyses of incoming signals to take place simultaneously. The last inference suggests that there may be segregation of information flow to central stations subserving particular functions.

Acoustic Maculae↗
PubMed1991

Network thermodynamic analysis of vasomotion in a microvascular network.

The modulation of microvascular blood flow by vasomotion in the individual vessels of a simple vascular network was simulated by means of a network thermodynamic model. The flow is driven under a pulsating pressure through two arcades of branching vasoactive arterioles into a passive resistance representing the capillary and venular beds. Each vessel was assumed to have the capability of decreasing rhythmically the local diameter over a short section by a specified fraction of the maximum value and to change the average diameter along its total length in response to alterations in intraluminal pressure. Blood was assumed to exhibit a simple linear viscous flow resistance. Alterations in flow rate and distribution through the network were determined as a function of the magnitude and frequency of vasomotion within the individual arterioles supplying blood to the microvascular bed. Specific cases are shown to illustrate how blood flow can be influenced by the patterns of vasomotion within the network.

Arterioles↗
PubMedDate not supplied

[The organization of the network characteristics of the cells in local and distributed neuronal networks of the cat brain].

In cats with elaborated alimentary instrumental reflexes to light net characteristics of neurones of visual, motor cortex and the hypothalamus lateral nucleus were studied on the basis of revealed interneuronal interactions by means of cross-correlation method of analysis. Different organization of net properties of the cortical neurones in organization of local and distributed neuronal networks was shown, namely: predominance of the divergent characteristics over the convergent ones for cells in local networks and levelling of these relations in distributed nets. Neurones of the lateral hypothalamus nucleus had equal presentation of divergent and convergent properties in organization of local and distributed networks. Net characteristics of neurones of the cortical and subcortical structures were manifested in the background after the elaboration and the extinction of conditioned reflexes. Only small cells of the visual cortex were functionally dependent and changed correlation of net characteristics in local networks at CR extinction.

Animals↗
PubMed1992

Computer simulation of the segmental neural network generating locomotion in lamprey by using populations of network interneurons.

Realistic computer simulations of the experimentally established local spinal cord neural network generating swimming in the lamprey have been performed. Populations of network interneurons were used in which cellular properties, like cell size and membrane conductance including voltage dependent ion channels were randomly distributed around experimentally obtained mean values, as were synaptic conductances (kainate/AMPA, NMDA, glycine) and delays. This population model displayed more robust burst activity over a wider frequency range than the more simple subsample model used previously, and the pattern of interneuronal activity was appropriate. The strength of the reciprocal inhibition played a very important role in the regulation of burst frequency, and just by changing the inhibitory bias the entire physiological range could be covered. At the lower frequency range of bursting the segmental excitatory interneurons provide stability as does the activation of voltage dependent NMDA receptors. Spike frequency adaptation by means of summation of afterhyperpolarization (AHP) serves as a major burst terminating factor, and at lower rates the membrane properties conferred by the NMDA receptor activation. The lateral interneurons were not of critical importance for the burst termination. They may, however, be of particular importance for inducing a rapid burst termination during for instance steering and righting reactions. Several cellular factors combine to provide a secure and stable motor pattern in the entire frequency range.

Animals↗
PubMed1992

A network thermodynamic model of the concentrating properties of the rabbit/rat kidney in the steady state using the electronic network simulation program SPICE.

A model for the simulation of the diluting and concentrating properties of the rabbit and rat kidney is developed. Translation of the physical model into an electronic one brings the model into a form that can be handled by the electronic network simulation program SPICE. The steady state responses of both kidneys to various inputs are calculated under certain conditions.

Animals↗
PubMed1992

Certification of cholesterol measurements by the National Reference Method Laboratory Network with routine clinical specimens: effects of network laboratory bias and imprecision.

The National Reference Method Laboratory Network has initiated a program to certify clinical laboratory cholesterol measurement performance by using routine clinical specimens. Clinical laboratory and reference laboratory measurements of split samples are used to assess whether the clinical laboratory is meeting the Laboratory Standardization Panel's goals for accuracy and precision. We used a computer-based Monte Carlo simulation model of split-sample proficiency testing to evaluate the certification program and, in particular, to analyze the effects of reference laboratory bias and imprecision. Results of our simulations indicate that the accuracy of the certification program is strongly influenced by reference laboratory bias and less influenced by reference laboratory imprecision. The certification program is potentially highly accurate, but unless reference laboratory bias is tightly controlled, the number of classification errors may limit its utility. Moreover, the decision limit of the certification program needs to be higher than the Laboratory Standardization Panel's goal (3.5% instead of 3.0%) to ensure that an acceptably high proportion of well-performing clinical laboratories can become certified.

Certification↗