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PubMed2006

A network-based analysis of allergen-challenged CD4+ T cells from patients with allergic rhinitis.

We performed a network-based analysis of DNA microarray data from allergen-challenged CD4(+) T cells from patients with seasonal allergic rhinitis. Differentially expressed genes were organized into a functionally annotated network using the Ingenuity Knowledge Database, which is based on manual review of more than 200,000 publications. The main function of this network is the regulation of lymphocyte apoptosis, a role associated with several genes of the tuber necrosis factor superfamily. The expression of TNFRSF4, one of the genes in this family, was found to be 48 times higher in allergen-challenged cells than in diluent-challenged cells. TNFRSF4 is known to inhibit apoptosis and to enhance Th2 proliferation. Examination of a different material of allergen-stimulated peripheral blood mononuclear cells showed a higher number of interleukin-4(+) type 2 CD4(+) T (Th2) cells in patients than in controls (P<0.01), as well as a higher number of non-apoptotic Th2 cells in patients (P<0.01). The number of Th2 cells expressing TNFRSF4, TNFSF7 and TNFRSF1B was also significantly higher in patients. Treatment with anti-TNFSF4 resulted in a significantly decreased number of Th2 cells (P<0.05). A logical inference from all this is that the proliferation of allergen-challenged Th2 cells is associated with a decreased apoptosis of Th2 cells and an increase in TNFRSF4 signalling.

Adolescent↗

PubMed2025

Stress-induced altered expression of hippocampal nuclear and mitochondrial encoded genes in rats and cross-species genetic associations reveal molecular links to depression.

BACKGROUND: Mitochondria play a pivotal role in energy production, and their dysfunction not only hampers cells' ability to meet energy requirements but also contributes to the impairment of neural plasticity, a critical feature of depressive disorders. In this study, mitochondrial cross-omics analysis was carried out in the hippocampus of restraint rats to understand the role of mitochondria in depression pathophysiology. METHODS: The expression profiles of hippocampal mitochondrial and nuclear-encoded genes in mitochondrial fractions from restraint and handled control rats were obtained using high-throughput RNA sequencing. Weighted gene co-expression network analysis (WGCNA) was used to identify the gene co-expression and pathways associated with the restraint phenotype. Mutual Information Network algorithm tools Arance, CLR, and MRNET were additionally used to screen the functional modules and hub genes and their similarity with the WGCNA-based network analysis. Finally, cross-species homology followed by gene association analysis was conducted to obtain SNPs and haplotypes related to depression phenotype. RESULTS: A significant proportion of mitochondrial and nuclear-encoded genes showed differential regulation in the hippocampus of restraint rats. WGCNA and Mutual Information Network analysis yielded distinct functional modules significantly related to restraint phenotype. Further network analysis revealed distinct co-expression patterns associated with differentially expressed genes associated with these modules. Cross-species analysis showed 39 significantly associated SNPs with the depression phenotype, where the most significant SNP, rs10899570, was located within the TENM4 gene. Further, rs1573529 and rs10899570 were distributed into the linkage disequilibrium block where SNPs were highly correlated. Subsequent haplotype analysis showed that rs1573529 and rs10899570 were significantly associated with depressive behavior. CONCLUSIONS: The study demonstrates a significant impact of restraint stress on mitochondrial functions and genetic association, suggesting their critical role in depression pathophysiology.

Animals↗

PubMed2026

Identifying JAK2 and ANXA5 as Key Genes Linking Obstructive Sleep Apnea and Oxidative Stress via Machine Learning and Multilayer Transcriptomic Integration With Functional Validation.

Obstructive sleep apnea (OSA) is a common and severe sleep disorder closely associated with oxidative stress (OS). This study aims to identify and validate potential OS-related genes associated with OSA through bioinformatics methods. We successfully identified OS-related differentially expressed genes (OS-DEGs) by combining the limma test, weighted correlation network analysis (WGCNA), and OS-related genes from the GeneCards database. Key genes and potential biological roles were further identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), enrichment analysis, protein-protein interaction (PPI) network analysis, Lasso regression analysis, random forest algorithm, and support vector machine recursive feature elimination (SVM-RFE) method. Evaluate and validate the accuracy of key genes through receiver operating characteristic (ROC) curve analysis. The human single-cell RNA sequencing (scRNA-seq) dataset is used for cell classification annotation, analysis of key gene single-cell expression profiles, and virtual gene knockout experiments based on the scTenifoldKnk algorithm. Integrating scRNA-seq sequencing, pseudotime trajectory inference, cell-cell communication analysis, and bulk immune infiltration deconvolution reveals monocyte subtype remodeling in OSA. Finally, the expression levels of key genes in clinical samples were validated using real-time quantitative PCR (RT-qPCR) and Western blotting. A total of 57 common DEGs, indicating significant enrichment in OS, inflammation, and tumor pathways, particularly prominent in the immunometabolism pathway. By integrating DEGs, WGCNA, PPI results, and machine learning methods, key genes Janus kinase 2 (JAK2) and ANXA5 were screened out. JAK2 was significantly upregulated under disease conditions, while ANXA5 was significantly downregulated. ROC curve exhibited high accuracy (area under the curve [AUC] > 0.85). Human scRNA-seq analysis revealed that key genes were predominantly highly expressed in monocytes. Virtual knockout experiments demonstrated that these key genes play a crucial role in regulating immune responses and inflammatory reactions. PPI networks and enrichment analysis verified that downstream genes S100P, ALOX5AP, PROK2, and PADI4 may collaboratively participate in immune response and inflammation regulation. Finally, clinical sample experiment further validated the results of bioinformatics analysis. This study provides new research insights for the diagnosis, mechanism research, and treatment development of OSA in the future by integrating multilayer transcriptomic and machine learning techniques.

Humans↗

PubMed2026

Identification of mitochondrial energy metabolism-related candidate genes UQCR10 and NDUFA6 in pediatric tetralogy of fallot: an exploratory bioinformatics study.

BACKGROUND: Tetralogy of Fallot (TOF) is one of the most common cyanotic congenital heart diseases in infants and young children. Its molecular basis remains incompletely understood. This study aimed to identify mitochondrial energy metabolism-related candidate genes associated with pediatric TOF using public heart tissue transcriptomic datasets from the GEO database. METHODS: Datasets GSE146218 and GSE217772 were downloaded and merged, followed by batch-effect correction. Differential expression analysis was performed to identify differentially expressed genes (DEGs). Functional enrichment analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network analysis were used to prioritize candidate genes. The Comparative Toxicogenomics Database (CTD) was used as an exploratory literature-based tool to summarize gene-disease associations. RESULTS: A total of 960 DEGs were identified. Functional enrichment analyses showed that these genes were mainly enriched in mitochondrial energy metabolism-related pathways, including oxidative phosphorylation and the mitochondrial respiratory chain. WGCNA and PPI network analyses further prioritized UQCR10 and NDUFA6 as candidate genes, and both genes showed increased expression in TOF heart tissue samples. CTD analysis suggested literature-based associations between these genes and cardiovascular or developmental disease-related terms. CONCLUSION: This exploratory bioinformatics study identified UQCR10 and NDUFA6 as mitochondrial energy metabolism-related candidate genes upregulated in pediatric TOF heart tissue. These findings suggest that mitochondrial respiratory chain-related transcriptional alterations may be involved in TOF-associated myocardial remodeling or stress responses. Further experimental and clinical validation is required to confirm their biological relevance.

Humans↗

PubMed2004

Rapid and simple quantitative measurement of alpha-fetoprotein by combining immunochromatographic strip test and artificial neural network image analysis system.

BACKGROUND: Quantitative immunochromatographic strip (ICS) assay can facilitate clinical diagnosis by providing more information than traditional qualitative or semiquantitative strip assay. METHODS: We constructed a human serum alpha-fetoprotein (AFP) measurement system by combining semiquantitative ICS tests and artificial neural network (ANN) image analysis system [immunochromatographic strip analyzed by artificial neural network image analysis system (IAIS)]. After ICS tests completed, the AFP concentration can be obtained from analysis of IAIS software. The equipment required are commercial semiquantitative AFP dipstick, optical scanner, centrifuge (option), personal computer, and software of IAIS. RESULTS: The serum AFP concentrations measured by IAIS were strongly correlated (r = 0.9971) with that by RIA. Using Bland-Altman analysis, the IAIS achieved clinical acceptable limits of agreement in comparison with RIA. The within-run precision of IAIS, expressed as coefficients of variation (CV), at 81.7 ng/ml was 1.50% and, at 244.4 ng/ml, was 1.09%. The measurement of serum AFP by IAIS can be completed in 20 min. CONCLUSIONS: The newly constructed quantitative immunochromatographic strip assay (IAIS) is a simple, rapid, and reliable method for serum AFP measurement. With the simple equipment required, the IAIS can be performed outside the laboratory and is ideal for outpatient or point-of-care AFP testing.

Biomarkers, Tumor↗

PubMed1995

Mitochondrial portraits of human populations using median networks.

Analysis of variation in the hypervariable region of mitochondrial DNA (mtDNA) has emerged as an important tool for studying human evolution and migration. However, attempts to reconstruct optimal intraspecific mtDNA phylogenies frequently fail because parallel mutation events partly obscure the true evolutionary pathways. This makes it inadvisable to present a single phylogenetic tree at the expense of neglecting equally acceptable ones. As an alternative, we propose a novel network approach for portraying mtDNA relationships. For small sample sizes (< approximately 50), an unmodified median network contains all most parsimonious trees, displays graphically the full information content of the sequence data, and can easily be generated by hand. For larger sample sizes, we reduce the complexity of the network by identifying parallelisms. This reduction procedure is guided by a compatibility argument and an additional source of phylogenetic information: the frequencies of the mitochondrial haplotypes. As a spin-off, our approach can also assist in identifying sequencing errors, which manifest themselves in implausible network substructures. We illustrate the advantages of our approach with several examples from existing data sets.

Biological Evolution↗

PubMed2003

Network component analysis: reconstruction of regulatory signals in biological systems.

High-dimensional data sets generated by high-throughput technologies, such as DNA microarray, are often the outputs of complex networked systems driven by hidden regulatory signals. Traditional statistical methods for computing low-dimensional or hidden representations of these data sets, such as principal component analysis and independent component analysis, ignore the underlying network structures and provide decompositions based purely on a priori statistical constraints on the computed component signals. The resulting decomposition thus provides a phenomenological model for the observed data and does not necessarily contain physically or biologically meaningful signals. Here, we develop a method, called network component analysis, for uncovering hidden regulatory signals from outputs of networked systems, when only a partial knowledge of the underlying network topology is available. The a priori network structure information is first tested for compliance with a set of identifiability criteria. For networks that satisfy the criteria, the signals from the regulatory nodes and their strengths of influence on each output node can be faithfully reconstructed. This method is first validated experimentally by using the absorbance spectra of a network of various hemoglobin species. The method is then applied to microarray data generated from yeast Saccharamyces cerevisiae and the activities of various transcription factors during cell cycle are reconstructed by using recently discovered connectivity information for the underlying transcriptional regulatory networks.

Biology↗

PubMed1992

Relationships between secondary structure fractions for globular proteins. Neural network analyses of crystallographic data sets.

The relationship between the fractions of protein secondary structural components as determined from X-ray crystallographic data by the procedures of Kabsch and Sander (KS) and of Levitt and Greer (LG) is analyzed by neural network analysis of these two tabulations of literature data. A linear relationship between the KS and LG reductions of X-ray data to secondary structure descriptors is demonstrated by a regression analysis of the relationships between these sets of structural parameters. Back-propagation neural network analysis was then used to derive equations for determination of the most probable fractions of beta-sheet, bend, turn, and "other" conformations given the fraction of alpha-helix in a globular protein. The deviation of the X-ray values for beta-sheet from that determined with these equations was shown to have a variance that exponentially decreased with increasing fraction of alpha-helix. A second neural network analysis showed that knowledge of both the alpha-helical and beta-sheet fractions in a protein significantly reduces the uncertainty in prediction of the other components of the secondary structure. These analyses provide insight into the nature of the data sets derived from crystal structures. Since these complications of crystal structure data are commonly used as reference information for quantitative evaluation of spectra (for example, FTIR, Raman, and electronic or vibrational circular dichroism) in terms of secondary structure, such internal correlations in the reference sets may have significant effects on the stability of spectroscopic analyses derived from them.

Mathematics↗

PubMed1994

Reduction of false positives in computerized detection of lung nodules in chest radiographs using artificial neural networks, discriminant analysis, and a rule-based scheme.

A computer-aided diagnosis (CAD) scheme is being developed to identify image regions considered suspicious for lung nodules in chest radiographs to assist radiologists in making correct diagnoses. Automated classifiers--an artificial neural network, discriminant analysis, and a rule-based scheme--are used to reduce the number of false-positive detections of the CAD scheme. The CAD scheme first detects nodule candidates from chest radiographs based on a difference image technique. Nine image features characterizing nodules are extracted automatically for each of the nodule candidates. The extracted image features are then used as input data to the classifiers for distinguishing actual nodules from the false-positive detections. The performances of the classifiers are evaluated by receiver-operating characteristic analysis. On the basis of the database of 30 normal and 30 abnormal chest images, the neural network achieves an AZ value (area under the receiver-operating-characteristic curve) of 0.79 in detecting lung nodules, as tested by the round-robin method. The neural network, after being trained with a training database, is able to eliminate more than 83% of the false-positive detections reported by the CAD scheme. Moreover, the combination of the trained neural network and a rule-based scheme eliminates 96% of the false-positive detections of the CAD scheme.

Diagnosis, Computer-Assisted↗

PubMed2006

A likelihood approach to analysis of network data.

Biological, sociological, and technological network data are often analyzed by using simple summary statistics, such as the observed degree distribution, and nonparametric bootstrap procedures to provide an adequate null distribution for testing hypotheses about the network. In this article we present a full-likelihood approach that allows us to estimate parameters for general models of network growth that can be expressed in terms of recursion relations. To handle larger networks we have developed an importance sampling scheme that allows us to approximate the likelihood and draw inference about the network and how it has been generated, estimate the parameters in the model, and perform parametric bootstrap analysis of network data. We illustrate the power of this approach by estimating growth parameters for the Caenorhabditis elegans protein interaction network.

Animals↗

PubMed2024

Network proximity analysis as a theoretical model for identifying potential novel therapies in primary sclerosing cholangitis.

Primary Sclerosing Cholangitis (PSC) is a progressive cholestatic liver disease with no licensed therapies. Previous Genome Wide Association Studies (GWAS) have identified genes that correlate significantly with PSC, and these were identified by systematic review. Here we use novel Network Proximity Analysis (NPA) methods to identify already licensed candidate drugs that may have an effect on the genetically coded aspects of PSC pathophysiology.Over 2000 agents were identified as significantly linked to genes implicated in PSC by this method. The most significant results include previously researched agents such as metronidazole, as well as biological agents such as basiliximab, abatacept and belatacept. This in silico analysis could potentially serve as a basis for developing novel clinical trials in this rare disease.

Cholangitis, Sclerosing↗

PubMedDate not supplied

Sustainable network advantages: a game theoretic approach to community-based health care coalitions.

Health care organizations often enter into a cooperative arrangement to create safety-net programs and coordinate care. Maintaining effective cooperation in such alliances poses special problems that can be examined using network analysis and explained in game theory terms. A mental health coalition case study is presented using network analysis and game theory interpretations. Had a positive-sum game approach been applied to the coalition's initial design, its subsequent suboptimal performance might have been averted. The application of network analysis plus a game theoretic paradigm has significant implications for improving both the design and the coordination of such coalitions.

Community Health Planning↗

PubMed2004

Brain anatomy in Turner syndrome: evidence for impaired social and spatial-numerical networks.

Analysis of brain structure in Turner syndrome (TS) provides the opportunity to identify the consequences of the loss of one X chromosome on brain anatomy and to characterize the neural bases underlying the specific cognitive profile of TS subjects which includes deficits in spatial-numerical processing and social cognition. Fourteen subjects with TS and fourteen controls were investigated using voxel-based analysis of high resolution anatomical and diffusion tensor images and using sulcal morphometry. The analysis of anatomical images provided evidence for macroscopical changes in cortical regions involved in social cognition such as the left superior temporal sulcus and orbito-frontal cortex and in a region involved in spatial and numerical cognition such as the right intraparietal sulcus. Diffusion tensor images showed a displacement of the grey-white matter interface of the left and right superior temporal sulcus and revealed bilateral microstructural anomalies in the temporal white matter. The analysis of fiber orientation suggests specific alterations of fiber tracts connecting posterior to anterior temporal regions. Last, sulcal morphometry confirmed the anomalies of the left and right superior temporal sulci and of the right intraparietal sulcus. Our results thus provide converging evidence of regionally specific structural changes in TS that are highly consistent with the hallmark symptoms associated with TS.

Adolescent↗

PubMed2025

Interaction of host gene-gut microbiota in male grading of Macrobrachium rosenbergii.

UNLABELLED: The giant freshwater prawn (GFP; Macrobrachium rosenbergii), a crustacean of high nutritional and economic value, is crucial for aquaculture. During the same growth cycle, male GFPs develop into three distinct forms: small males, orange claw males, and blue claw males. These morphotypes display varying social behaviors, which severely constrain their industrial development. To address this, this study collected male GFP samples at critical developmental time points (100, 110, and 120 days post-hatching) for phenotypic trait measurement and analysis to obtain external morphological data. Through gut microbiota diversity analysis, we identified key gut bacteria (Lactococcus garvieae and Lactobacillus taiwanensis) influencing male morphotype differentiation. Transcriptomic analysis revealed host Kyoto Encyclopedia of Gene and Genome pathways and key genes (Wnt-6, CTSB, CTSL, PPAE, and TP53) associated with morphotype differentiation. The interactions among phenotypic traits, gut microbiota, and key genes were systematically studied through association analysis. Weighted gene co-expression network analysis was employed to construct co-expression modules, from which critical gene modules influencing phenotypic variation were identified. Through association network analysis, we established an "Achromobacter-CD-TRINITY_DN93139_c0_g2 (calpain clp-1)" interaction model. Our findings provide novel insights into the genetic enhancement of GFPs and offer guidelines for future research regarding gut symbiotic bacteria and breeding initiatives. IMPORTANCE: Male Macrobrachium rosenbergii (giant freshwater prawn [GFP]) in the same growth cycle will develop into small males, orange claw males, and blue claw males. This individual heterogeneity in growth significantly impacts the benefits of aquaculture. However, the factors influencing the differentiation of male GFP morphotype remain unclear. This study analyzed the phenotypic data of various GFP levels, the structure of the intestinal microbiota, and the differential genes within the gonadal transcriptome at critical time points of male GFP-level type differentiation. The aim was to explore the potential role of intestinal microbiota and differential genes in this phenomenon. This study offers new insights into the research on the phenomenon of male GFP-level type differentiation.

Animals↗

PubMed1993

The chromatin network: image analysis of differentiating chick embryo chondrocytes.

The chromatin network was revealed in DNA stained cell imprints of developing chick embryo chrondrocytes using the recently developed method of TV image processing. The network consists of two classes of alveoles. The large alveoles, which accompany large chromocentres, are concentrated at the centromeric nuclear pole representing the most stable part of the network. The small alveoles, connected with the small chromocentres and located around the chromosome arms and telomeric ends, are dynamic. These smaller alveoles appear in differentiating chondroblasts and disappear in ageing chondrocytes. Ectopic conjunctions involving constitutive and intercalary heterochromatin are responsible for the network formation.

Algorithms↗

PubMed2007

Analysis of news of the Japanese asbestos panic: a supposedly resolved issue that turned out to be a time bomb.

BACKGROUND: Asbestos-linked public health problems were widely reported in Japan, in 2005. The objective is to apply text mining with network analysis to characterize these problems. METHODS: Text mining with network analysis of newspaper headlines including the word 'asbestos' published in 1987 and 2005 was conducted. Outcome measures are occurrence of the words and simultaneous occurrence of two words in the newspaper headlines. RESULTS: In 36 headlines, which contained the word 'asbestos' in 1987, the word 'pollution' (40%) appeared most frequently, followed by 'removal' (31%) and 'campaign' (29%). For combinations of words, the following occurred most frequently: 'campaign and expulsion' (26%) followed by 'removal and campaign' (14%). Of 293 headlines in 2005, the following words appeared: 'hazard' (31%), 'person' (16%) and 'death' (13%). For combinations, the following appeared: 'person and death' (9%). Asbestos pollution and removal campaigns were reported in 1987, but the death of citizens was reported in 2005. CONCLUSIONS: Text mining with network analysis, which presents one of the methods for visualization of text data, suggests the following insight. Insufficient steps against asbestos had been taken for 20 years, which is compatible with the latency period. It has resulted in widespread exposure to asbestos and more severe asbestos-related public health problems among citizens. This methodology suggests that analyzing text data by this method can serve future surveillance and efficient use of epidemiological knowledge.

Asbestos↗

PubMed2001

Comparison of logistic regression and neural networks to predict rehospitalization in patients with stroke.

CONTEXT: Rehospitalization following inpatient medical rehabilitation has important health and economic implications for patients who have experienced a stroke. OBJECTIVE: Compare logistic regression and neural networks in predicting rehospitalization at 3-6-month follow-up for patients with stroke discharged from medical rehabilitation. DESIGN: The study was retrospective using information from a national database representative of medical rehabilitation patients across the US. SETTING: Information submitted to the Uniform Data System for Medical Rehabilitation from 1997 and 1998 by 167 hospital and rehabilitation facilities from 40 states was examined. PARTICIPANTS: 9584 patient records were included in the sample. The mean age was 70.74 years (SD = 12.87). The sample included 51.6% females and was 77.6% non-Hispanic White with an average length of stay of 21.47 days (SD = 15.47). MAIN OUTCOME MEASURES: Hospital readmission from 80 to 180 days following discharge. RESULTS: Statistically significant variables (P <.05) in the logistic model included sphincter control, self-care ability, age, marital status, ethnicity and length of stay. Area under the ROC curves were 0.68 and 0.74 for logistic regression and neural network analysis, respectively. The Hosmer-Lemeshow goodness-of-fit chi-square was 11.32 (df = 8, P = 0.22) for neural network analysis and 16.33 (df = 8, P = 0.11) for logistic regression. Calibration curves indicated a slightly better fit for the neural network model. CONCLUSION: There was no statistically significant or practical advantage in predicting hospital readmission using neural network analysis in comparison to logistic regression for persons who experienced a stroke and received medical rehabilitation during the period of the study.

Aged↗

PubMed1996

Network thermodynamics: analysis and synthesis of membrane transport system.

The bond graph expression of network thermodynamics is a useful tool for modeling of biological systems [1, 8, 9, 13]. Epithelial transport systems, such as frog skin and the acinar cell of the salivary gland, have been modeled and simulated using this tool [8, 9]. However, thermodynamic description of a complex system has been considered difficult [1], and doubt has also been cast on the applicability of network thermodynamics to thermal or entropy relations [2]. In this review, I discussed the thermodynamic basis for network thermodynamics using the availability function and assuming local equilibrium [7]. I also showed the applicability of bond graphs to the analysis of nonisothermal systems which include thermal conduction, thermoosmotic and thermoelectric phenomena [13]. Thus, network thermodynamics is suitable for analysis of more complicated systems and for computer simulation.

Animals↗