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 1,621 records · Page 90Linked to original sources
PubMed2005

Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues.

MOTIVATION: Global protein interaction network (interactome) analysis provides an effective way to understand the relationships between genes. Through this approach, it was demonstrated that the essential genes in yeast tend to be highly connected as well as connected to other highly connected genes. This is in contrast to the genes that are not essential, which share neither of these properties. Using a similar interactome-transcriptome approach, the topological features in the interactome of differentially expressed genes in lung squamous cancer tissues are assessed. RESULTS: This analysis reveals that the genes that are differentially elevated, as obtained from the microarray gene profiling data, in cancer are well connected, whereas the suppressed genes and randomly selected ones are less so. These results support the notion that a topological analysis of cancer genes using protein interaction data will allow the placement of the list of genes, often of the disparate nature, into the global, systematic context of the cell. The result of this type of analysis may provide the rationale for therapeutic targets in cancer treatment.

Algorithms↗
PubMed2003

[Internet-based conjoint analysis with the example of medical networks].

The present paper will demonstrate the usefulness of conjoint analysis in the area of medicine, a method that is well-known in consumer research. The aim was to investigate the preference structure of network-oriented physicians regarding competitive consulting and information offerings in the context of a hypothetical network between a hospital and office-based physicians. A combination of qualitative ("grounded theory") and quantitative ("conjoint analysis") methods was used. Combined items from the field of information, consultation, advanced training and financing were available. The following combination was preferred: attendance of a medical specialist in the outpatient department (factor "consultation"), the receipt of brief physician info letters (factor "information"), the possibility of discussing one's own patients with other physicians (factor "advanced training") and self-financing such an offer (factor "financing").

Humans↗
PubMed2026

The Addition of Concurrent Immune Checkpoint Inhibitors for Chemoradiotherapy With Consolidative Immune Checkpoint Inhibitors in Unresectable Cancers: A Systematic Review and Meta-Analysis.

Following the success of chemoradiotherapy (CRT) combined with consolidative immune checkpoint inhibitors (ICIs) in locally advanced tumors, over 30 ongoing randomized controlled trials (RCTs) are investigating the potential benefits of adding concurrent ICIs. To investigate the differences in efficacy and safety between adding and not adding concurrent ICIs to CRT followed by consolidative ICIs, a literature search was conducted in PubMed, Embase, and the Cochrane Library, incorporating RCTs comparing CRT combined with consolidative ICIs versus CRT alone, or CRT with both concurrent and consolidative ICIs versus CRT alone. The primary outcomes were overall survival (OS) and progression-free survival (PFS). To reduce potential bias, an additional mirror-design analysis was performed through network meta-analysis. A total of 13 RCTs comprising 6868 patients and 14 cohort studies comprising 4724 patients were included. While patients treated with CRT and consolidative ICIs demonstrated significantly superior OS and PFS to patients treated with CRT alone in RCTs (HR of OS, 0.743, 95% CI, 0.654-0.843; HR of PFS, 0.674, 95% CI, 0.577-0.786), CRT and concurrent-plus-consolidative ICIs did not improve OS and PFS compared with CRT alone (HR of OS, 0.942, 95% CI, 0.782-1.134; HR of PFS, 0.880, 95% CI, 0.752-1.030). Significant differences were detected in OS (p = 0.038) and PFS (p = 0.017) between CRT combined with consolidative ICIs treatment versus CRT combined with concurrent and consolidative ICIs treatment from RCTs. In conclusion, adding concurrent ICIs may dampen the survival benefits of CRT combined with consolidative ICIs. This evidence informs future RCT design strategies.

Humans↗
PubMed1992

Noninvasive diagnosis of coronary artery disease using a neural network algorithm.

This study examines the utility of neural networks for detecting coronary artery disease noninvasively by using the clinical examination variables and extracting useful information from the diastolic heart sounds associated with coronary occlusions. It has been widely reported that coronary stenoses produce sounds due to the turbulent blood flow in these vessels. These complex and highly attenuated signals taken from recordings made in both soundproof and noisy rooms were detected and analyzed to provide feature set based on the poles and power spectral density function (PSD) of the Autoregressive (AR) method after Adaptive Line Enhancement (ALE) method. In addition, some physical examination variables such as sex, age, body weight, smoking condition, diastolic pressure, systolic pressure and derivation from them were included in the feature vector. This feature vector was used as the input pattern to the neural network. The analysis was studied on one hundred recordings (63 abnormal, 37 normals). The network correctly identified 84% of the subjects with coronary artery disease and 89% of the normal subjects.

Algorithms↗
PubMed2004

Reconstruction of microbial transcriptional regulatory networks.

Although metabolic networks can be readily reconstructed through comparative genomics, the reconstruction of regulatory networks has been hindered by the relatively low level of evolutionary conservation of their molecular components. Recent developments in experimental techniques have allowed the generation of vast amounts of data related to regulatory networks. This data together with literature-derived knowledge has opened the way for genome-scale reconstruction of transcriptional regulatory networks. Large-scale regulatory network reconstructions can be converted to in silico models that allow systematic analysis of network behavior in response to changes in environmental conditions. These models can further be combined with genome-scale metabolic models to build integrated models of cellular function including both metabolism and its regulation.

Bacteria↗
PubMed1996

Variability of energy metabolism and nuclear T3-receptors within the skeletal muscle tissue of pigs different with respect to the halothane gene.

Energy metabolism of skeletal muscle tissue of pigs growing from approximately 12 to 18 kg (12 homozygous halothane negative, HH; 16 heterozygotes, Hh; 17 homozygous halothane susceptible, hh) was measured in vivo using 31P nuclear magnetic resonance (NMR) spectroscopy. Data for intracellular pH, phosphocreatine (PCr), phosphomonoesters (PME), and ATP were analyzed by canonical discriminant analysis, an artificial neural network approach, and analysis of variance. Within the hh pigs, two subpopulations could be distinguished before the application of halothane treatment. Some of the hh pigs had a high PME concentration in the biceps femoris muscle (hh(pme+)), whereas others had a low concentration (hh(pme-)) (2.18 +/- .12 for hh(pme+) vs 1.68 +/- .12 mM for hh(pme-), P < .004). The hh(pme+) pigs were statistically different from HH pigs for pH (P < .03), PME (P < .004), and PCr (P < .008) before halothane treatment. The hh(pme-) pigs were not different from the Hh and HH pigs with respect to PME when measured before halothane treatment (P > .05). However, intracellular pH (P < .03) and PCr (P < .008) of the hh(pme-) pigs were different from those of HH pigs (7.15 vs 7.19 for pH and 38.7 vs 35.1 for PCr, respectively). When combining intracellular pH, PME, and PCr within a canonical discriminant analysis, all were measured before halothane treatment, Hh pigs were found to be different from HH pigs (Mahalanobis distance different from zero, P < .02). In a second experiment, growth rate, depth of longissimus muscle, and maximal binding capacity of nuclear T3-receptors of skeletal muscle tissue were different (P < .05, P < .002, and P < .02, respectively) among pigs selected from the same genetic lines. Of the variability in depth of the longissimus muscle, 22% was explained by variability in maximal binding capacity of nuclear T3-receptors. These results, if confirmed with a large number of pigs, might open new possibilities for selection procedures for leanness because, with respect to halothane susceptibility, a shift between genotypic and phenotypic variability was observed.

Adenosine Triphosphate↗
PubMedDate not supplied

Classification of in vivo 1H MR spectra from breast tissue using artificial neural networks.

BACKGROUND: The study was designed in order to investigate whether artificial neural networks could be used for analysis of in vivo magnetic resonance (MR) spectra from breast cancer patients. MATERIALS AND METHODS: In vivo 1H MR spectra with three different echo times (TE 135, 350 and 450 msec) were acquired from patients with benign and malignant breast lesions and from healthy volunteers, of whom some were breast-feeding. A spectral region (4.0-1.5 ppm) was used as input for artificial neural network analysis, for the attempted classification of the data into different groups. RESULTS: Data recorded at all three echo times were necessary to obtain the best results. Furthermore, malignant tissue was differentiated from benign tumours using this approach, whereas benign tumours were poorly separated from healthy tissue. CONCLUSION: The results presented here indicate that in vivo MR spectroscopy in conjunction with neural network analysis might be useful for the evaluation of breast lesions.

Breast↗
PubMed2005

Using large-scale perturbations in gene network reconstruction.

BACKGROUND: Recent analysis of the yeast gene network shows that most genes have few inputs, indicating that enumerative gene reconstruction methods are both useful and computationally feasible. A simple enumerative reconstruction method based on a discrete dynamical system model is used to study how microarray experiments involving modulated global perturbations can be designed to obtain reasonably accurate reconstructions. The method is tested on artificial gene networks with biologically realistic in/out degree characteristics. RESULTS: It was found that a relatively small number of perturbations significantly improve inference accuracy, particularly for low-order inputs of one or two genes. The perturbations themselves should alter the expression level of approximately 50-60% of the genes in the network. CONCLUSIONS: Time-series obtained from perturbations are a common form of expression data. This study illustrates how gene networks can be significantly reconstructed from such time-series while requiring only a relatively small number of calibrated perturbations, even for large networks, thus reducing experimental costs.

Algorithms↗
PubMed2001

Extravascular contribution of blood oxygenation level-dependent signal changes: a numerical analysis based on a vascular network model.

In this study, the extravascular (EV) contribution to blood oxygenation level-dependent (BOLD) signal changes was evaluated, based on a numerical analysis of the nondimensional Bloch-Torrey equation for a vascular network model of randomly oriented paramagnetic vessels. From a statistical point of view, the susceptibility-induced EV BOLD signal changes for both gradient- and spin-echoes are shown to be proportional to cerebral blood volume (CBV). These changes can be expressed by a function of two nondimensional parameters: one represents the characteristic phase change of the signal induced by the deoxyhemoglobin-containing vessels, and the other represents a diffusion effect relative to the vessel radius. The numerical results showed excellent agreement with the analytical solutions obtained previously for the regimes of small and large diffusion effects. Based on the numerical results for gradient- and spin-echoes, it is possible to show the dependence of the EV BOLD contribution on the echo time, the static magnetic field strength, and the stimulation-induced CBV change. This dependence can be demonstrated for vessels of small, intermediate, and large radii, which correspond respectively to the slow, intermediate, and fast diffusion regimes. A more realistic functional MRI (fMRI) simulation which includes an intravascular (IV) contribution is presented, with emphasis on the field strength dependence of the BOLD signal changes.

Electromagnetic Fields↗
PubMed2002

An artificial intelligence approach to classify and analyse EEG traces.

We present a fully automatic system for the classification and analysis of adult electroencephalograms (EEGs). The system is based on an artificial neural network which classifies the single epochs of trace, and on an Expert System (ES) which studies the time and space correlation among the outputs of the neural network; compiling a final report. On the last 2000 EEGs representing different kinds of alterations according to clinical occurrences, the system was able to produce 80% good or very good final comments and 18% sufficient comments, which represent the documents delivered to the patient. In the remaining 2% the automatic comment needed some modifications prior to be presented to the patient. No clinical false-negative classifications did arise, i.e. no altered traces were classified as 'normal' by the neural network. The analysis method we describe is based on the interpretation of objective measures performed on the trace. It can improve the quality and reliability of the EEG exam and appears useful for the EEG medical reports although it cannot totally substitute the medical doctor who should now read the automatic EEG analysis in light of the patient's history and age.

Adult↗
PubMed1993

A new approach to the study of skin vascularization.

There are several methods of quantifying the vascularization of tissues, including the skin, but they are imprecise in terms of quantification of the complexity and structure of vascular networks. Fractal analysis can quantify the complexity of any structure existing in nature by using fractional dimension. This study makes a case for this approach by demonstrating the fractal structure of the skin vascular network in the mouse. The skin was removed from the posterior face of the thigh, which is supplied by a musculocutaneous perforator system. Twenty arterial vascular networks were investigated by image analysis and the fractal dimension was determined by the box counting method. Statistical analysis revealed an average mean of fractal dimension D = 1.256 (SD = 0.086), indicating low to intermediate complexity with a narrow distribution of results. D should logically fluctuate within a certain limit, depending on the anatomical structure investigated and its physiological function. These results demonstrate the ability of fractal analysis to quantify the vascular pattern of the skin. Fractal analysis opens a new field of investigation in the study of vascularization patterns and possible vascular modification by different physiological or pathological conditions (flap-delay techniques, tobacco use, diabetes mellitus, classification of diabetic retinopathies).

Animals↗
PubMed2000

Analysis of the asynchronous state in networks of strongly coupled oscillators

A general method is presented for the analysis of the asynchronous state in networks of identical, all-to-all coupled, limit-cycle oscillators of arbitrary dimension and with arbitrarily strong coupling. It is shown that, with strong coupling, this state can be destabilized in directions orthogonal to the limit cycle, which may change the units' behavior qualitatively. An example, involving integrate and fire neurons with spike adaptation, exhibits a bifurcation to a synchronized bursting state for strong feedback coupling. The analysis can account for transitions that cannot be studied in the commonly used phase-coupled approximation.

Journal Article↗
PubMed2005

Determination of compound aminopyrine phenacetin tablets by using artificial neural networks combined with principal components analysis.

A method for simultaneous, nondestructive analysis of aminopyrine and phenacetin in compound aminopyrine phenacetin tablets with different concentrations has been developed by principal component artificial neural networks (PC-ANNs) on near-infrared (NIR) spectroscopy. In PC-ANN models, the spectral data were initially analyzed by principal component analysis. Then the scores of the principal components were chosen as input nodes for the input layer instead of the spectral data. The artificial neural network models using the spectral data as input nodes were also established and compared with the PC-ANN models. Four different preprocessing methods (first-derivative, second-derivative, standard normal variate (SNV), and multiplicative scatter correction) were applied to three sets of NIR spectra of compound aminopyrine phenacetin tablets. The PC-ANNs approach with SNV preprocessing spectra was found to provide the best results. The degree of approximation was performed as the selective criterion of the optimum network parameters.

Aminopyrine↗
PubMed1999

Blind separation of multichannel electrogastrograms using independent component analysis based on a neural network.

The electrogastrogram (EGG) is an abdominal surface measurement of gastric myo-electrical activity which regulates gastric contractions. It is of great clinical importance to record and analyse multichannel EGGs, which provide more information on the propagation and co-ordination of gastric contractions. EGGs are, however, contaminated by myo-electric interference from other organs and artefacts such as motion and respiration. The aim of the study is to separate the gastric signal from noisy multichannel EGGs without any information on the interference, using independent component analysis. A neural-network model is proposed, and corresponding unsupervised learning algorithms are developed to achieve the separation. The performance of the proposed method is investigated using artificial data simulating real EGG signals. Experimental EGG data are obtained from humans and dogs. The processed results of both simulated and real EGG data show the following: first, the proposed method is able to separate normal gastric slow waves from respiratory artefacts and random noises. It is also able to extract gastric slow waves, even when the EGG is contaminated by severe respiratory and ECG artefacts. Secondly, when the stomach contains various gastric electric signals with different frequencies, the proposed method is able to separate these different signals, as illustrated by simulations. These data suggest that the proposed method can be used to separate gastric slow waves, respiratory and motion artefacts, and intestinal myo-electric interference that are mixed in the EGG. It can also be used to detect gastric slow-wave uncoupling, during which the stomach has multiple gastric signals with different frequencies. It is believed that the proposed method may also be applicable to other biomedical signals.

Adult↗
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

Stability analysis of delayed cellular neural networks.

In this paper, the problems of stability in a class of delayed cellular neural networks (DCNN) are studied; some new stability criteria are obtained by using the Lyapunov functional method and some analysis techniques. These criteria can be used to design globally stable networks and thus have important significance in both theory and application.

Journal Article↗
PubMed2006

VANTED: a system for advanced data analysis and visualization in the context of biological networks.

BACKGROUND: Recent advances with high-throughput methods in life-science research have increased the need for automatized data analysis and visual exploration techniques. Sophisticated bioinformatics tools are essential to deduct biologically meaningful interpretations from the large amount of experimental data, and help to understand biological processes. RESULTS: We present VANTED, a tool for the visualization and analysis of networks with related experimental data. Data from large-scale biochemical experiments is uploaded into the software via a Microsoft Excel-based form. Then it can be mapped on a network that is either drawn with the tool itself, downloaded from the KEGG Pathway database, or imported using standard network exchange formats. Transcript, enzyme, and metabolite data can be presented in the context of their underlying networks, e. g. metabolic pathways or classification hierarchies. Visualization and navigation methods support the visual exploration of the data-enriched networks. Statistical methods allow analysis and comparison of multiple data sets such as different developmental stages or genetically different lines. Correlation networks can be automatically generated from the data and substances can be clustered according to similar behavior over time. As examples, metabolite profiling and enzyme activity data sets have been visualized in different metabolic maps, correlation networks have been generated and similar time patterns detected. Some relationships between different metabolites were discovered which are in close accordance with the literature. CONCLUSION: VANTED greatly helps researchers in the analysis and interpretation of biochemical data, and thus is a useful tool for modern biological research. VANTED as a Java Web Start Application including a user guide and example data sets is available free of charge at http://vanted.ipk-gatersleben.de.

Algorithms↗
PubMed1993

How to see characteristics of structural parameters in QSAR analysis: descriptor mapping using neural networks.

In addition to its outstanding abilities in both classification and fitting, the neural network can also accurately predict the values of the untrained region. To rationalize this ability of prediction, the authors mathematically discussed the valid region of prediction. Based on such a background, the authors proposed "descriptor mapping" in the QSAR analysis, which visualizes the nonlinear dependencies between structural parameters. A variable of the linear multiple regression analysis in the QSAR study is supposed to be linear to the biological intensity and is independent of other variables. Analysis by the descriptor mapping method discloses the reality.

Anticarcinogenic Agents↗