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.

SourceResearch connectionFrom yearThrough year

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

At least 1,639 records · Page 91Linked to original sources

PubMed1998

Predicting outcome of anterior temporal lobectomy using simulated neural networks.

PURPOSE: Anterior temporal lobectomy (ATL) is an important option for treatment of medically refractory seizures. Patient selection is not always clear-cut, and there is inherent morbidity and mortality associated with the invasive and expensive surgical protocols. To determine whether patient selection might be facilitated by application of artificial intelligence, we developed a model that predicted seizure outcome after ATL, using a simulated neural network (SNN). METHODS: Predictions of the model were compared with predictions derived from conventional discriminant function analysis. Neural networks and discriminant functions were devised that would predict the occurrence of both Class 1 outcomes (totally seizure-free), and Class 1 or Class 2 outcomes (nearly or totally seizure-free), using data from 87 patients from three surgical centers. The SNNs and discriminant functions were developed using data from a randomly selected subsample of 65 patients, and both models were cross-validated, using the remaining 22 patients. RESULTS: The discriminant functions showed overall predictive accuracy of 78.5% and 72.7%, while the neural networks demonstrated overall accuracy of 81.8% and 95.4%. CONCLUSIONS: Simulated neural networks show promise as adjuncts to decision-making in the selection of epilepsy surgery patients.

Adolescent↗

PubMed2000

Evaluating the frequency rate of hypomagnesemia in critically ill pediatric patients by using multiple regression analysis and a computer-based neural network.

OBJECTIVES: To determine the frequency rate of hypomagnesemia in patients admitted to the pediatric intensive care unit (ICU), and to identify subsets of patients (grouped by disease) who are at greatest risk of hypomagnesemia. We also compared a neural network model with multiple regression analysis to identify independent variables that would correlate with hypomagnesemia and to predict serum magnesium values in critically ill pediatric patients overall. DESIGN: Prospective, multicenter study. SETTING: Tertiary level medical/surgical pediatric ICUs. PATIENTS: Data were obtained at admission to the pediatric ICU for 463 patients from newborn to 18 yrs old who were admitted with a variety of surgical and nonsurgical conditions. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Total serum magnesium values were obtained within the first 24 hrs after admission in 463 pediatric patients admitted to four pediatric ICUs. Hypomagnesemia (defined as total serum magnesium <0.75 mmol/L) was found in 51 (11%) of the 463 patients, with the highest frequency rate (72%) and lowest mean serum magnesium level (0.66 +/- 0.17 mmol/L) in patients admitted after surgery with extensive osseous involvement (spinal fusion and craniofacial reconstruction). To determine whether hypomagnesemia could be predicted on the basis of other laboratory and clinical criteria, multiple regression analysis was performed and showed age, weight, and albumin levels weakly associated (r2 = .14, p < .001) with magnesium levels within the different diagnostic groups. These data were used to produce a mathematical model able to predict magnesium levels within 5% of the actual values in 23% of patients. A neural network was also created to compare its predictive capabilities to those of the multiple regression model. Once trained on a random subset (85%) of the patient population, the neural network was able to predict magnesium levels to within 5% of actual values for 88% of the remaining 15% of patients, comparing favorably with the predictions derived from the multiple regression model. CONCLUSIONS: Hypomagnesemia is not uncommon (11%) in critically ill pediatric patients, but is very common (72%) in patients admitted after surgery for spinal fusion or craniofacial reconstruction. Patients who undergo surgery for correction of scoliosis and craniofacial anomalies should have serum magnesium levels monitored closely after surgery. In other patients, a neural network or multiple regression model could help predict which patients would be at risk of developing hypomagnesemia, thereby focusing testing on patients likely to benefit from such testing.

Adolescent↗

PubMed1991

Neural network approach to detection of metastatic melanoma from chromatographic analysis of urine.

Chromatographic analysis of sera or urine is important in medicine for the evaluation of patients whose clinical status is associated with the presence of specific biochemical markers. Malignant melanoma has been a model for such studies due to the elaboration of melanin precursors and pigment as the tumor metastasizes. Computer-assisted methods for categorizing chromatographic data and clinical status are imperative due to the large number of detectable compounds and possible correlations. In addition, computer-based analysis of the data can readily extract patterns that are not obvious by visual inspection. In this paper, we present a neural network analysis of melanoma chromatographic and clinical data that categorizes subjects into normals, NED patients (No Evidence of Disease), and metastatic patients. The set of marker compounds for metastatic disease represents a significant advance over the correlations derived by visual inspection.

Algorithms↗

PubMed1996

A discrete firing event analysis of the adaptive cluster expansion network.

This paper describes how a hierarchical network for encoding sensor data (the adaptive cluster expansion network) can be constructed by linking together a number of elementary modules, each of which is a simple two-layer encoder/decoder network. To achieve this goal, a Bayesian analysis is applied to the discrete neural firing events that occur within each layer of the network.

Journal Article↗

PubMed2004

Elucidation and structural analysis of conserved pools for genome-scale metabolic reconstructions.

In this article, we introduce metabolite concentration coupling analysis (MCCA) to study conservation relationships for metabolite concentrations in genome-scale metabolic networks. The analysis allows the global identification of subsets of metabolites whose concentrations are always coupled within common conserved pools. Also, the minimal conserved pool identification (MCPI) procedure is developed for elucidating conserved pools for targeted metabolites without computing the entire basis conservation relationships. The approaches are demonstrated on genome-scale metabolic reconstructions of Helicobacter pylori, Escherichia coli, and Saccharomyces cerevisiae. Despite significant differences in the size and complexity of the examined organism's models, we find that the concentrations of nearly all metabolites are coupled within a relatively small number of subsets. These correspond to the overall exchange of carbon molecules into and out of the networks, interconversion of energy and redox cofactors, and the transfer of nitrogen, sulfur, phosphate, coenzyme A, and acyl carrier protein moieties among metabolites. The presence of large conserved pools can be viewed as global biophysical barriers protecting cellular systems from stresses, maintaining coordinated interconversions between key metabolites, and providing an additional mode of global metabolic regulation. The developed approaches thus provide novel and versatile tools for elucidating coupling relationships between metabolite concentrations with implications in biotechnological and medical applications.

Algorithms↗

PubMed2000

Data evaluation for soft drink quality control using principal component analysis and back-propagation neural networks.

This work describes an alternative for chemical data research, with the aim of evaluating finished product quality. Analytical data for additives in soft drinks are interpreted by the use of multivariate data analysis: principal component analysis (PCA), factor analysis, cluster analysis, and artificial neural networks. Taking into account various chemical components like sorbic, benzoic, and ascorbic acids; saccharose; caffeine; Na, K, Ca, Mg, Fe, Zn, Cu, P, and B, soft drinks were characterized and classified. The ratios of Na, K, Ca + Mg, P, and K/Na have been studied. The application of PCA, cluster analysis, and artificial neural networks showed that combination of these chemometric tools offers effective means for modeling and classifying soft drinks in accordance with their contents in additives and heavy metals.

Beverages↗

PubMed2005

Elucidation of directionality for co-expressed genes: predicting intra-operon termination sites.

MOTIVATION: In this paper, we present a novel framework for inferring regulatory and sequence-level information from gene co-expression networks. The key idea of our methodology is the systematic integration of network inference and network topological analysis approaches for uncovering biological insights. RESULTS: We determine the gene co-expression network of Bacillus subtilis using Affymetrix GeneChip time-series data and show how the inferred network topology can be linked to sequence-level information hard-wired in the organism's genome. We propose a systematic way for determining the correlation threshold at which two genes are assessed to be co-expressed using the clustering coefficient and we expand the scope of the gene co-expression network by proposing the slope ratio metric as a means for incorporating directionality on the edges. We show through specific examples for B. subtilis that by incorporating expression level information in addition to the temporal expression patterns, we can uncover sequence-level biological insights. In particular, we are able to identify a number of cases where (1) the co-expressed genes are part of a single transcriptional unit or operon and (2) the inferred directionality arises due to the presence of intra-operon transcription termination sites. AVAILABILITY: The software will be provided on request. SUPPLEMENTARY INFORMATION: http://www.phys.psu.edu/~ralbert/pdf/gma_bioinf_supp.pdf

Algorithms↗

PubMed2005

Signaling through receptors and scaffolds: independent interactions reduce combinatorial complexity.

After activation, many receptors and their adaptor proteins act as scaffolds displaying numerous docking sites and engaging multiple targets. The consequent assemblage of a variety of protein complexes results in a combinatorial increase in the number of feasible molecular species presenting different states of a receptor-scaffold signaling module. Tens of thousands of such microstates emerge even for the initial signal propagation events, greatly impeding a quantitative analysis of networks. Here, we demonstrate that the assumption of independence of molecular events occurring at distinct sites enables us to approximate a mechanistic picture of all possible microstates by a macrodescription of states of separate domains, i.e., macrostates that correspond to experimentally verifiable variables. This analysis dissects a highly branched network into interacting pathways originated by protein complexes assembled on different sites of receptors and scaffolds. We specify when the temporal dynamics of any given microstate can be expressed using the product of the relative concentrations of individual sites. The methods presented here are equally applicable to deterministic and stochastic calculations of the temporal dynamics. Our domain-oriented approach drastically reduces the number of states, processes, and kinetic parameters to be considered for quantification of complex signaling networks that propagate distinct physiological responses.

Binding Sites↗

PubMed2024

Comprehensive analysis of circRNA-miRNA-mRNA network related to angiogenesis in recurrent implantation failure.

BACKGROUND: Abnormal endometrial blood flow causes a decrease in endometrial receptivity and is considered a relatively independent risk factor for recurrent implantation failure (RIF). This study aimed to explore the potentially functional circRNA-miRNA-mRNA network in RIF, and further explore its mechanism. METHODS: Datasets were downloaded from the GEO database to identify differentially expressed circRNAs, miRNAs and mRNAs. The circRNA-miRNA-mRNA and PPI networks were constructed using Cytoscape 3.6.0 and the STRING database, the hub genes were identified with the cytoHubba plug-in, and a circRNA-miRNA-hub mRNA regulatory sub-network was constructed. Then, GO and KEGG pathway enrichment analyses of the hub genes were performed to comprehensively analyze the mechanism of hub mRNAs in RIF. Due to the results of circRNAs-miRNAs-hub mRNAs regulatory network, we verified the expression of circRNA_0001721, circRNA_0000714, miR-17-5p, miR-29b-3p, HIF1A and VEGFA in the RIF mouse model by qRT‒PCR and western blotting. RESULTS: We initially identified 175 DEmRNAs, 48 DEmiRNAs and 56 DEcircRNAs in RIF associated with angiogenesis and constructed a circRNA-miRNA‒mRNA network and PPI network. We further identified six hub genes in the acquired network. Based on these genes, functional enrichment analysis revealed that the HIF-1 signaling pathway plays a vital role in endometrial angiogenesis in RIF. In addition, the interaction networks of circRNA_0001721/miR-17-5p/HIF1A and the circRNA_0000714/miR-29b-3p/VEGFA axis were predicted. In the RIF mouse model, circRNA_0001721, circRNA_0000714, HIF1A and VEGFA were down-regulated, whereas miR-17-5p and miR-29b-3p were up-regulated according to qRT‒PCR and western blotting. CONCLUSION: This study revealed that the HIF-1 signaling pathway plays a vital role in endometrial angiogenesis in RIF. The circRNA_0001721/miR-17-5p/HIF1A and circRNA_0000714/miR-29b-3p/VEGFA axes might play a role in the pathogenesis of endometrial angiogenesis in RIF.

MicroRNAs↗

PubMed2005

ChiroSensor -- an array of non-invasive sEMG electrodes.

It is shown that the statistical analysis of the sEMG signals recorded by the ChiroSensors along the paraspinal muscles during Network Spinal Analysis (NSA) provides objective confirmation of the physiological reality as perceived by the practitioner as it relates to levels of care and spinal injury recovery.

Electrodes↗

PubMed2000

Graph-set and packing analysis of hydrogen-bonded networks in polyamide structures in the Cambridge Structural Database.

The hydrogen-bond networks and crystal packing of 81 unique secondary di- and polyamides in the Cambridge Structural Database are investigated. Graph-set analysis, as implemented in the RPluto program, is used to classify network motifs. These have been rationalized in terms of the relative dispositions of the amide groups. Peptide and retropeptides exhibit significant conformational flexibility, which permits alternative hydrogen-bonding patterns. In peptides, dihedral angles of -psi approximately varphi approximately 105 degrees allow an antiparallel ladder arrangement, containing rings of either the same or alternating sizes. For retropeptides, and diamides with an odd number of CH(2) spacers, this conformation leads to a parallel ladder with rings of equal size. If varphi approaches -60 degrees and psi 180 degrees, ladders adopt a helical twist, and if the conformation is distorted further, a three-dimensional network is usually adopted. Diamides with aromatic or an even number of CH(2) spacers generally form either antiparallel ladders or sheets, although some exhibit both polymorphs. Symmetry relationships within and between hydrogen-bonded chains, ladders and sheets in the crystal packing have also been analysed. Polyamides form considerably more complex networks, although many of the structural motifs present in the diamides occur as components of these networks.

Journal Article↗

PubMed1997

Stability analysis of a class of noise perturbed neural networks.

We establish robustness stability results for a specific type of artificial neural networks for associative memories under parameter perturbations and determine conditions that ensure the existence of asymptotically stable equilibria of the perturbed neural system that are the asymptotically stable equilibria of the original unperturbed neural network. The proposed stability analysis tool is the sliding mode control and it facilitates the analysis by considering only a reduced-order system instead of the original one and time-dependent external stimuli.

Association Learning↗

PubMed2006

Identification of functional modules in a PPI network by clique percolation clustering.

Large-scale experiments and data integration have provided the opportunity to systematically analyze and comprehensively understand the topology of biological networks and biochemical processes in cells. Modular architecture which encompasses groups of genes/proteins involved in elementary biological functional units is a basic form of the organization of interacting proteins. Here we apply a graph clustering algorithm based on clique percolation clustering to detect overlapping network modules of a protein-protein interaction (PPI) network. Our analysis of the yeast Sacchromyces cerevisiae suggests that most of the detected modules correspond to one or more experimentally functional modules and half of these annotated modules match well with experimentally determined protein complexes. Our method of analysis can of course be applied to protein-protein interaction data for any species and even other biological networks.

Algorithms↗

PubMed2003

A multi-scale probabilistic network model for detection, synthesis and compression in mammographic image analysis.

We develop a probabilistic network model over image spaces and demonstrate its broad utility in mammographic image analysis, particularly with respect to computer-aided diagnosis. The model employs a multi-scale pyramid decomposition to factor images across scale and a network of tree-structured hidden variables to capture long-range spatial dependencies. This factoring makes the computation of the density functions local and tractable. The result is a hierarchical mixture of conditional probabilities, similar to a hidden Markov model on a tree. The model parameters are found with maximum likelihood estimation using the expectation-maximization algorithm. The utility of the model is demonstrated for three applications: (1) detection of mammographic masses for computer-aided diagnosis; (2) qualitative assessment of model structure through mammographic synthesis; and (3) compression of mammographic regions of interest.

Female↗

PubMed1986

Application of network planning methodology to workflow analysis in a reference laboratory.

Efficient organization of the workflow in a reference laboratory is essential to timely resolution of serological problems. This study investigated the usefulness of network planning methods to streamline workflow in a blood center reference laboratory. The investigation of a patient serological problem which included a positive direct antiglobulin test was chosen for analysis. Individual steps in the resolution process were identified. All possible logical sequences of these steps were investigated to determine which steps were along the 'critical path', the sequence of steps which is rate-limiting. By implementing new procedures for these steps, faster resolution of a problem should result. The Program Evaluation Review Technique predicted that altering procedures to shorten 3 steps (autoadsorption, antibody identification panel at 37 degrees C and screening for antigen-negative units) would result in a decrease in the time required to complete a patient problem by 29%. Time-motion studies documented a 35% improvement in problem resolution time with the new procedures (p greater than 0.05). Utilization of the network planning techniques in an immunohematological laboratory may allow for more efficient and expedient operation.

Clinical Laboratory Techniques↗

PubMed1988

Interinstitutional variability in DNA flow cytometric analysis of tumors. The National Cancer Institute's Flow Cytometry Network Experience.

Flow cytometric DNA analysis of human urinary bladder specimens may be clinically useful for prognosis in transitional cell (urothelial) carcinoma and for detecting recurrence after treatment. However, many important methodological differences exist among institutions which have described this technique, and it has not previously been shown that data from different institutions are comparable. The National Cancer Institute has created a Flow Cytometry Network to address the need for technology assessment of flow cytometry. This report describes the independent flow cytometric analysis and interpretation of "unknown" paraffin-embedded bladder tumor specimens by the five Network institutions. Although important differences in method existed among the institutions, substantial agreement was achieved in actual data generated and their interpretation. This suggests that a consensus regarding acceptable laboratory performance of this technique could be reached, which should facilitate its more widespread clinical implementation.

Carcinoma, Transitional Cell↗

PubMed1997

[Identification of hashish samples with inductively coupled high-frequency plasma emission spectrometry and neutron activation analysis and data handling with neuronal networks. 1. Methods for the quantitative determination of characteristic trace elements].

Neutron activation analysis (NAA) and inductively coupled plasma emission spectrometry (ICP-AES) were used to quantify the relative contents of Fe, Sc, Ce, Pa, Cr, Co, respectively the absolute contents of Cr, Zn, Mn, Fe, Mg, Al, Cu, Ti, Ca, Sr in hashish samples, seized in different countries. The samples were processed after dry ashing by means of instrumental NAA and after wet mineralization by means of ICP-AES. For determination of the sampling and measurement errors, one of the samples was analyzed repeatedly with both methods. Classifying hashish samples with regard to concentration of certain elements could be done by artificial neural networks with a modified backpropagation algorithm. By this way, identity and non identity of one unknown sample with one of many different samples as data pool can be ascertained, on principle.

Cannabis↗

PubMed2006

Neural networks in frontotemporal dementia--a meta-analysis.

OBJECTIVE: Frontotemporal dementia is the most common form of frontotemporal lobar degeneration. It is characterized by deep alterations in behavior and personality. We conducted a systematic and quantitative meta-analysis to examine its neural correlates and place the disease in a framework of cognitive neuropsychiatry. METHODS: MedLine and Current Contents search engines were used to identify imaging studies investigating frontotemporal dementia between 1980 and 2005. Nine studies were identified reporting either atrophy or decreases in glucose utilization. Finally, the analysis involved 132 patients and 166 controls. A quantitative meta-analysis was performed. Maxima of the studies resulted in activation likelihood estimates. RESULTS: The meta-analysis revealed a particularly frontomedian network impaired in frontotemporal dementia. Additionally, right anterior insula, and medial thalamus were identified. CONCLUSIONS: Our study specifies frontotemporal dementia as the frontomedian variant of frontotemporal lobar degeneration. The disease affects neural networks enabling self-monitoring, theory of mind capabilities, processing/evaluation of internal mental states, perception of pain and emotions, and sustaining personality and self. Our study contributes to placing frontotemporal dementia in cognitive neuropsychiatry.

Aged↗