Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “network analysis”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

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

At least 397 records · Page 22Linked to original sources
PubMed2002

Tc-99m ethylene cysteinate dimer SPECT in the differential diagnosis of parkinsonism.

Positron emission tomography (PET) and network analysis have been used to identify a reproducible pattern of regional metabolic covariation that is associated with idiopathic Parkinson's disease (PD). The activity of this PD-related pattern can be quantified in individual subjects and used to discriminate PD patients from atypical parkinsonians. Because PET is not commonly available, we sought to determine whether similar discrimination could be achieved using more routine single photon emission computed tomography (SPECT) perfusion methods. Twenty-three subjects with PD (age, 63 +/- 9 years), 22 subjects with multiple system atrophy (MSA; age, 64 +/- 7 years), and 20 age-matched healthy controls (age, 62 +/- 13 years) underwent SPECT imaging of regional cerebral perfusion with Tc-99m ethylene cysteinate dimer (ECD). Using network analysis, we determined whether a PD-related pattern existed in the SPECT data, and whether its expression discriminated PD from MSA patients. Additionally, we compared the accuracy of group discrimination achieved by this pattern with that of the PET-derived PD-related pattern applied to the SPECT data. Network analysis of the SPECT data identified a significant pattern characterized by relative increases in cerebellar, lentiform, and thalamic perfusion covarying with decrements in the frontal operculum and in the medial temporal cortex. Subject scores for this pattern discriminated PD patients from controls (P < 0.01) and from MSA patients (P < 0.03). Subject scores for the PET-derived PD-related pattern computed in the individual SPECT scans more accurately distinguished PD patients from controls (P < 0.005) and from MSA patients (P = 0.0002). A significant PD-related covariance pattern can be identified in SPECT perfusion data. Moreover, the disease related pattern identified previously with PET can be applied to individual SPECT perfusion scans to provide group discrimination between PD patients, healthy controls, and individuals with MSA. Because of significant individual subject overlap between groups, however, the clinical utility of this method in the differential diagnosis of Parkinsonism remains uncertain.

Aged↗
PubMed2002

Layered neural networks based analysis of radon concentration and environmental parameters in earthquake prediction.

A layered neural network (LNN) has been employed to estimate the radon concentration in soil related to the environmental parameters. This technique can find any functional relationship between the radon concentration and the environmental parameters. Analysis of the data obtained from a site in Thailand indicates that this approach is able to differentiate time variation of radon concentration caused by environmental parameters from those arising by anomaly phenomena in the earth (e.g. earthquake). This method is compared with a linear computational technique based on impulse responses from multivariable time series. It is indicated that the proposed method can give a better estimation of radon variations related to environmental parameters that may have a non-linear effect on the radon concentration in soil, such as rainfall.

Disaster Planning↗
PubMed2006

Comparison of manual sleep staging with automated neural network-based analysis in clinical practice.

We have compared sleep staging by an automated neural network (ANN) system, BioSleep (Oxford BioSignals) and a human scorer using the Rechtschaffen and Kales scoring system. Sleep study recordings from 114 patients with suspected obstructed sleep apnoea syndrome (OSA) were analysed by ANN and by a blinded human scorer. We also examined human scorer reliability by calculating the agreement between the index scorer and a second independent blinded scorer for 28 of the 114 studies. For each study, we built contingency tables on an epoch-by-epoch (30 s epochs) comparison basis. From these, we derived kappa (kappa) coefficients for different combinations of sleep stages. The overall agreement of automatic and manual scoring for the 114 studies for the classification {wake / light-sleep / deep-sleep / REM} was poor (median kappa = 0.305) and only a little better (kappa = 0.449) for the crude {wake / sleep} distinction. For the subgroup of 28 randomly selected studies, the overall agreement of automatic and manual scoring was again relatively low (kappa = 0.331 for {wake light-sleep / deep-sleep REM} and kappa = 0.505 for {wake / sleep}), whereas inter-scorer reliability was higher (kappa = -0.641 for {wake / light-sleep / deep-sleep / REM} and kappa = 0.737 for {wake / sleep}). We conclude that such an ANN-based analysis system is not sufficiently accurate for sleep study analyses using the R&K classification system.

Adult↗
PubMed1993

Analytical methods to differentiate similar electroencephalographic spectra: neural network and discriminant analysis.

Differences in electroencephalographic (EEG) power spectra obtained under similar, but not identical, conditions may be difficult to discern using standard techniques. Statistical analysis may not be useful because of the large number of comparisons necessary. Visual recognition of differences also may be difficult. A new technique, neural network analysis, has been used successfully in other problems of pattern recognition and classification. We examined a number of methods of classifying similar EEG data: standard statistical analysis (analysis of variance), visual recognition, discriminant analysis, and neural network analysis. Twenty-nine volunteers received either thiopental (n = 9), midazolam (n = 10), or propofol (n = 10) in sedative doses in 3 different studies. These drugs produced very similar changes in the EEG power spectra. Except for beta 2 power during thiopental infusion, differences between drugs could not be detected using analysis of variance. Visual categorization was correct in 72% of the baseline EEGs, 70% of thiopental EEGs, 27% of propofol EEGs, and 46% of midazolam EEGs. A classification neural network (Learning Vector Quantization network) containing a Kohonen hidden layer was able to successfully classify 57 of 58 EEG samples (of 4 minutes' duration). Discriminant analysis had a similar rate of success. This level of performance was achieved by dividing the EEG power spectrum from 1 to 30 Hz into 15 2-Hz bandwidths. When the EEG power spectrum was divided into the "classical" frequency bandwidths (alpha, beta 1, beta 2, theta, delta), both neural network and discriminant analysis performance deteriorated. By training the network using only certain inputs we were able to identify drug-specific bandwidths that seemed to be important in correct classification. We conclude that propofol, thiopental, and midazolam produce different effects on the EEG and that both neural network and discriminant analysis are useful in identifying these differences. We also conclude that EEG spectra should be analyzed without using classical EEG bands (alpha, beta, etc.). Additionally, neural networks can be used to identify frequency bands that are "important" in specific drug effects on the EEG. Once a classification algorithm is obtained using either a neural network or discriminant analysis, it could be used as an on-line monitor to recognize drug-specific EEG patterns.

Adult↗
PubMed2001

Patterns of chlamydia and gonorrhea infection in sexual networks in Manitoba, Canada.

BACKGROUND: The use of sexual network analysis has the potential to further our understanding of sexually tranmitted disease (STD) epidemics and contribute to the development of more effective targeted control strategies. GOAL: To use sexual network analysis to study transmission patterns of chlamydia and gonorrhea in Manitoba, Canada. STUDY DESIGN: Routinely collected case/contact information gathered by public health nurses was used to construct the sexual network. RESULTS: Components within the sexual network ranged in size from 2 to 82 people. Two types of components, designated radial and linear, were described. Large linear components resembled the theoretical structure of STD core groups. Geographic analysis of the largest components demonstrated the potential for STD transmission between isolated rural communities and within different areas of an urban center. CONCLUSIONS: The application of sexual network analysis on a provincial basis demonstrated the importance of a centralized, coordinated approach to STD control. The analysis highlights the need for a greater understanding of the causative factors promoting the formation of different component types, the homogeneity and heterogeneity of behaviors within and between components, and the temporal stability of these patterns.

Adolescent↗
PubMed2004

A scale of functional divergence for yeast duplicated genes revealed from analysis of the protein-protein interaction network.

BACKGROUND: Studying the evolution of the function of duplicated genes usually implies an estimation of the extent of functional conservation/divergence between duplicates from comparison of actual sequences. This only reveals the possible molecular function of genes without taking into account their cellular function(s). We took into consideration this latter dimension of gene function to approach the functional evolution of duplicated genes by analyzing the protein-protein interaction network in which their products are involved. For this, we derived a functional classification of the proteins using PRODISTIN, a bioinformatics method allowing comparison of protein function. Our work focused on the duplicated yeast genes, remnants of an ancient whole-genome duplication. RESULTS: Starting from 4,143 interactions, we analyzed 41 duplicated protein pairs with the PRODISTIN method. We showed that duplicated pairs behaved differently in the classification with respect to their interactors. The different observed behaviors allowed us to propose a functional scale of conservation/divergence for the duplicated genes, based on interaction data. By comparing our results to the functional information carried by GO annotations and sequence comparisons, we showed that the interaction network analysis reveals functional subtleties, which are not discernible by other means. Finally, we interpreted our results in terms of evolutionary scenarios. CONCLUSIONS: Our analysis might provide a new way to analyse the functional evolution of duplicated genes and constitutes the first attempt of protein function evolutionary comparisons based on protein-protein interactions.

Computational Biology↗
PubMed2025

Identification of a PRDM1-regulated T cell network to regulate atherosclerotic plaque inflammation.

BACKGROUND: Inflammation is a key driver of atherosclerosis, yet the mechanisms sustaining inflammation in human plaques remain poorly understood. This study uses a network-based approach to identify immune gene programs involved in the transition from low- to high-risk (rupture-prone) human atherosclerotic plaques. METHODS: Expression data from human carotid artery plaques, both stable (low-risk, n&#x2009;=&#x2009;16) and unstable (high-risk, n&#x2009;=&#x2009;27), were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA). Bayesian network inference, operated on the eigengene values from the WGCNA, further extended the WGCNA analysis, and similarity to the signature of T cell subsets was validated in single-cell RNA sequencing data of human plaques, and a&#xa0;loss-of-function study in a mouse model of atherosclerosis. In silico drug repurposing was performed to identify potential therapeutic targets. RESULTS: Our analysis revealed a distinct gene module with a prominent T cell signature, particularly in unstable plaques. Key regulatory factors, RUNX3, IRF7 and in particular PRDM1, were significantly downregulated in plaque T cells from symptomatic versus asymptomatic patients, indicating a protective role. Additionally, as PRDM1 is downstream of IRF7, we opted for PRDM1 as a key target. T cell-specific Prdm1 deficiency in Western-type diet fed Ldlr knockout mice&#xa0;featured accelerated plaque progression. Finally, as PRDM1 targeting&#xa0;drugs are not yet available, we performed in silico drug repurposing, identifying EGFR inhibitors as promising therapeutic candidates. CONCLUSIONS: This study highlights a PRDM1-regulated T cell network that distinguishes high-risk from low-risk plaques and demonstrates the regulatory role of T cell PRDM1 in controlling atherosclerosis, positioning this pathway as a promising therapeutic target.

Plaque, Atherosclerotic↗
PubMed2006

A genome-scale assessment of peripheral blood B-cell molecular homeostasis in patients with rheumatoid arthritis.

OBJECTIVE: While rheumatoid arthritis (RA) is considered a prototypical autoimmune disease, the specific roles of B-cells in RA pathogenesis is not fully delineated. METHODS: We performed microarray expression profiling of peripheral blood B-cells from RA patients and controls. Data were analysed using differential gene expression analysis and 'gene networking' analysis (characterizing clusters of functionally inter-relelated genes) to identify both regulatory genes and the pathways in which they participate. Results were confirmed by quantitative real-time polymerase chain reaction and by measuring the levels of 10 serum cytokines involved in the pathways identified. RESULTS: Genes regulating and effecting the cell-cycle, proliferation, apoptosis, autoimmunity, cytokine networks, angiogenesis and neuro-immune regulation were differentially expressed in RA B-cells. Moreover, the serum levels of several soluble factors that modulate these pathways, including IL-1beta, IL-5, IL-6, IL-10, IL-12p40, IL-17 and VEGF were significantly increased in this cohort of RA patients. CONCLUSIONS: These results outline aspects of the multifaceted role B-cells play in RA pathogenesis in which immune dysregulation in RA modulates B-cell biology and thereby contributes to the induction and perpetuation of a pathogenic humoral immune response.

Adult↗
PubMed2003

Decision support for road system analysis and modification on the Tahoe National Forest.

The United States Forest Service is required to analyze road systems on each of the national forests for potential environmental impacts. We have developed a novel and inexpensive way to do this using the Ecosystem Management Decision Support program (EMDS). We used EMDS to integrate a user-developed fuzzy logic knowledge base with a grid-based geographic information system to evaluate the degree of truth for assertions about a road's environmental impact. Using spatial data for natural and human processes in the Tahoe National Forest (TNF, California, USA), we evaluated the assertion "the road has a high potential for impacting the environment." We found a high level of agreement between the products of this evaluation and ground observations of a TNF transportation engineer, as well as occurrences of road failures. We used the modeled potential environmental impact to negatively weight roads for a least-cost path network analysis to 1573 points of interest in the forest. The network analysis showed that out of 8233 km of road analyzed in the forest, 3483 km (42%) must be kept in a modified road network to ensure access to these points. We found that the modified network had improved patch characteristics, such as significantly fewer "cherry stem" roads intruding into patches, an improved area-weighted mean shape index, and larger mean patch sizes, as compared to the original network. This analysis system could be used by any public agency to analyze infrastructure for environmental or other risk and included in other mandated analyses such as risks to watersheds.

Conservation of Natural Resources↗
PubMed1999

Catastrophic forgetting in simple networks: an analysis of the pseudorehearsal solution.

Catastrophic forgetting is a major problem for sequential learning in neural networks. One very general solution to this problem, known as 'pseudorehearsal', works well in practice for nonlinear networks but has not been analysed before. This paper formalizes pseudorehearsal in linear networks. We show that the method can fail in low dimensions but is guaranteed to succeed in high dimensions under fairly general conditions. In this case an optimal version of the method is equivalent to a simple modification of the 'delta rule'.

Algorithms↗
PubMed2006

BISON: Bio-Interface for the Semi-global analysis Of Network patterns.

BACKGROUND: The large amount of genomics data that have accumulated over the past decade require extensive data mining. However, the global nature of data mining, which includes pattern mining, poses difficulties for users who want to study specific questions in a more local environment. This creates a need for techniques that allow a localized analysis of globally determined patterns. RESULTS: We developed a tool that determines and evaluates global patterns based on protein property and network information, while providing all the benefits of a perspective that is targeted at biologist users with specific goals and interests. Our tool uses our own data mining techniques, integrated into current visualization and navigation techniques. The functionality of the tool is discussed in the context of the transcriptional network of regulation in the enteric bacterium Escherichia coli. Two biological questions were asked: (i) Which functional categories of proteins (identified by hidden Markov models) are regulated by a regulator with a specific domain? (ii) Which regulators are involved in the regulation of proteins that contain a common hidden Markov model? Using these examples, we explain the gene-centered and pattern-centered analysis that the tool permits. CONCLUSION: In summary, we have a tool that can be used for a wide variety of applications in biology, medicine, or agriculture. The pattern mining engine is global in the way that patterns are determined across the entire network. The tool still permits a localized analysis for users who want to analyze a subportion of the total network. We have named the tool BISON (Bio-Interface for the Semi-global analysis Of Network patterns).

Journal Article↗
PubMed2026

Identification and expression validation of key genes of Xiaozhengtongluo formula in the treatment of diabetic nephropathy by Mendelian randomization.

Xiaozhengtongluo formula (XZTL) has a positive effect on the treatment of diabetic nephropathy (DN), but its mechanism is not fully understood. Therefore, it is important to explore the key genes of XZTL in the treatment of DN. Differentially expressed genes (DEGs) between DN and control obtained from GSE96804, drug target genes of XZTL, and disease target genes of DN obtained from public databases were intersected. Genes of intersection were defined as candidate genes. Next, Mendelian randomization (MR) analysis was used to ascertain the causal associations between candidate genes and DN. Afterwards, key genes were confirmed through receiver operating characteristic (ROC) curve analysis and expression validation. Subsequently, enrichment analysis, molecular regulatory network analysis, and molecular docking were conducted. Finally, experimental verification of the expression levels of key genes was performed through reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Altogether, 29 candidate genes were screened via MR analysis, identifying APOD, IGFBP3, and LPL as significantly associated with DN. IGFBP3 and APOD were risk factors, whereas LPL was protective. Consistent expression trends across training and validation datasets defined them as key genes. All three were co-enriched in 26 pathways, including oxidative phosphorylation. Regulatory networks showed MIR497HG/hsa-miR-19a-3p regulated IGFBP3, and NEAT1/hsa-miR-29a-3p regulated LPL; IGFBP3 and LPL were co-targeted by SP3 and SP1. Molecular docking revealed APOD-baicalein, LPL-oleic acid, and IGFBP3-quercetin binding, suggesting therapeutic potential. RT-qPCR confirmed aberrant expression of these genes in DN, which was normalized by XZTL intervention. In this study, three key genes (APOD, IGFBP3, and LPL) of XZTL in the treatment of DN were finally obtained, providing mechanistic clues for understanding XZTL's multi-target mechanism and providing experimentally tractable candidate targets for DN molecular subtyping, targeted therapeutic development, and precision medicine approaches in TCM.

Diabetic Nephropathies↗
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↗
PubMed2000

Nonlinear canonical correlation analysis by neural networks.

Canonical correlation analysis (CCA) is widely used to extract the correlated patterns between two sets of variables. A nonlinear canonical correlation analysis (NLCCA) method is formulated here using three feedforward neural networks. The first network has a double-barreled architecture, and an unconventional cost function, which maximizes the correlation between the two output neurons (the canonical variates). The remaining two networks map from the canonical variates back to the original two sets of variables. Tested on data sets with correlated nonlinear structures, NLCCA showed that the underlying nonlinear structures could be retrieved accurately under moderately noisy conditions. After a mode had been retrieved, NLCCA was applied to the residual to successfully retrieve the next mode. When tested for prediction skills, the NLCCA outperformed the CCA when the two sets of variables contained correlated nonlinear structures.

Artifacts↗
PubMed1993

A fractal model of HIV transmission on complex sociogeographic networks: towards analysis of large data sets.

"A paradigm of HIV (human immunodeficiency virus) transmission along very large 'sociogeographic' networks--spatially focused nets of social interaction--is extended to include fractal (dilationally self-similar) structures upon which a metric of 'sociogeographic' distance can be defined.... Techniques are sketched for determining the sociogeographic structure of a large, geographically centered social network, providing a possible empirical basis for predicting forms and rates of spread of the initial, rapid stages of an HIV outbreak for networks not yet infected, and perhaps greatly expanding the utility of routinely collected small-area administrative data sets in the design of mutually reinforcing, multifactorial disease-control strategies."

Communicable Diseases↗
PubMed2001

Early and intensive continuous hemofiltration for severe renal failure after cardiac surgery.

BACKGROUND: The aim of this study was to test whether early and intensive use of continuous venovenous hemofiltration (CVVH) achieved a better than predicted outcome in patients with severe acute renal failure undergoing cardiac operations, and whether a simple and yet accurate model could be developed to predict their outcome before starting CVVH. METHODS: Medical record analysis with collection of demographic, clinical, and outcome information was used. RESULTS: Sixty-five consecutive patients were treated with early and intensive CVVH (mean operation to CVVH time, 2.38 days; pump-controlled ultrafiltration rate, 2 L/h) after coronary artery bypass grafting (56.9%), single valve procedure (16.9%), or combined operations (26.2%). In 32.3% of patients, intraaortic balloon counterpulsation was required and 20% of patients were emergencies. Sustained hypotension despite inotropic and vasopressor support occurred in 40% of patients and prolonged mechanical ventilation in 58.5%. Using an outcome prediction score specific for acute renal failure, the predicted risk of death was 66%. Actual mortality was 40% (p = 0.003). Using multivariate logistic regression analysis and neural network analysis, patient outcome could be predicted with good levels of accuracy (receiver operating characteristic 0.89 and 0.9, respectively). CONCLUSIONS: Early and aggressive CVVH is associated with better than predicted survival in severe acute renal failure after cardiac operations. Using readily available clinical data, the outcome of such patients can be predicted before the implementation of CVVH.

Acute Kidney Injury↗
PubMed1998

Detection of lung injury with conventional and neural network-based analysis of continuous data.

OBJECTIVE: To test if analysis of pressure and flow waveform patterns with an artificial intelligence neural network could distinguish between normal and injured lungs. METHODS: Acute lung injury was induced in ten healthy anesthetized, mechanically ventilated dogs with repeated injections of oleic acid, until arterial blood oxyhemoglobin saturation reached 85% breathing room air. Airway pressure, esophageal pressure, airway flow, and arterial and mixed venous saturation signals were stored at 2 min intervals. Hemodynamic and blood gas data were collected every 10 min. Back-propagation neural networks were trained with normalized airway pressure and flow waveforms from normal and fully injured lungs. RESULTS: The networks scored lung injury on a continuous scale from +1 (normal) to -1 (injured). Network scores unequivocally distinguished between normal and fully injured lungs and suggested a gradual transition from normal to injury pattern. However, the response of the network was slow compared to compliance, resistance and venous admixture. CONCLUSIONS: Normal and fully injured lungs display distinct flow and pressure waveform patterns which are independent of changes in calculated pulmonary mechanics variables. These patterns can be recognized by a neural network. Further research is needed to determine the full potential of automated pattern recognition for lung monitoring.

Animals↗