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At least 181 records · Page 10Linked to original sources
PubMed2006

Identification of oxalotrophic bacteria by neural network analysis of numerical phenetic data.

A new approach with artificial neural network (ANN) was applied to numerical taxonomy of bacteria using the oxalate as carbon and energy source. For this aim the characters effective in differentiating separate groups were selected from morphological, physiological and biochemical test results. Fourteen aerobic, Gram-negative, oxalate-utilizing isolates and four oxalate-utilizing reference strains (Ralstonia eutropha DSM 428, Methylobacterium extorquens DSM 1337T, Ralstonia oxalatica DSM 1105T, Oxalicibacterium flavum DSM 15506T) were included in the study. ANN program used here was developed in Borland C++ language. Iterations were performed on an IBM compatible PC computer. ANN architecture having feed-forward backpropagation algorithm was used for teaching generalized delta rule. The results show that ANN can have a large potential in solving the taxonomic problems of oxalate-utilizing bacteria.

Gram-Negative Aerobic Bacteria↗
PubMed1995

Network analysis of PET-mapped visual pathways in Alzheimer type dementia.

Using path analysis to determine the systems-level neural networks mediating specific tasks from regional cerebral blood flow (rCBF) data obtained by positron emission tomography (PET), we recently found in young subjects strong functional linkages during a face matching task along a right hemisphere ventral network including occipital, temporal, and frontal regions. In this study, PET data obtained during a face matching task from mildly affected patients with dementia of the Alzheimer type (DAT) and healthy matched controls showed that (1) the neural model obtained in young subjects provides a good fit to data from old subjects; (2) although the DAT patients could perform this task with the same accuracy as controls, they did not use the same functional network.

Aged↗
PubMed2004

Artificial neural network analysis of gustatory responses in the thalamic taste relay of the rat.

We used an artificial neural network (ANN) as a model for analyzing single-neuron responses from the thalamic taste relay of rats. The network consisted of: (1) a layer of 44 input units, representing the responses of the 44 thalamic taste cells; (2) a layer of hidden units of varying numbers; and (3) a layer of four output units. We used the back-propagation algorithm to train the output units to discriminate among tastants based on inputs from the thalamic neurons. As the network became fully trained, we found that: (1) only two hidden units were necessary to provide nearly the full discriminative capacity of the network; (2) the loss of even a few of the input units that had the highest impact on hidden units caused a drastic reduction of discriminative power, implying that not all neurons contribute equally to the neural code; and (3) adding a temporal component to the input, by representing each 100-ms time bin as a separate input unit, increased the accuracy with which output units were able to identify tastants. We used data from behavioral discrimination tasks as a measure of the capacity of the network to identify stimuli correctly. A network with two hidden units was about as effective as an across-pattern analysis in accounting for the rat's discriminative ability.

Animals↗
PubMed2004

Neural network analysis in pharmacogenetics of mood disorders.

BACKGROUND: The increasing number of available genotypes for genetic studies in humans requires more advanced techniques of analysis. We previously reported significant univariate associations between gene polymorphisms and antidepressant response in mood disorders. However the combined analysis of multiple gene polymorphisms and clinical variables requires the use of non linear methods. METHODS: In the present study we tested a neural network strategy for a combined analysis of two gene polymorphisms. A Multi Layer Perceptron model showed the best performance and was therefore selected over the other networks. One hundred and twenty one depressed inpatients treated with fluvoxamine in the context of previously reported pharmacogenetic studies were included. The polymorphism in the transcriptional control region upstream of the 5HTT coding sequence (SERTPR) and in the Tryptophan Hydroxylase (TPH) gene were analysed simultaneously. RESULTS: A multi layer perceptron network composed by 1 hidden layer with 7 nodes was chosen. 77.5 % of responders and 51.2% of non responders were correctly classified (ROC area = 0.731--empirical p value = 0.0082). Finally, we performed a comparison with traditional techniques. A discriminant function analysis correctly classified 34.1 % of responders and 68.1 % of non responders (F = 8.16 p = 0.0005). CONCLUSIONS: Overall, our findings suggest that neural networks may be a valid technique for the analysis of gene polymorphisms in pharmacogenetic studies. The complex interactions modelled through NN may be eventually applied at the clinical level for the individualized therapy.

5' Untranslated Regions↗
PubMed1999

Is the social network analysis necessary in the rehabilitation of individuals with head injury?

A social network is a support system in which one can obtain assistance for simple daily needs and in emergency situations. A diminished social network can result in failure to integrate into normalized environments. The results of this investigation clearly showed that social networks among a group of 70 people who were traumatically brain injured had significantly fewer members than a control group of non-injured respondents. Additionally, the make-up of social networks was different. Specifically, respondents who were traumatically brain injured had social networks consisting mainly of family and staff members of the rehabilitation facility, while non-injured respondents had mostly friends or acquaintances, coworkers, and family members, in that order.

Adolescent↗
PubMed2004

Sexual network analysis of a gonorrhoea outbreak.

OBJECTIVES: Sexual partnerships can be viewed as networks in order to study disease transmission. We examined the transmission of Neisseria gonorrhoeae in a localised outbreak in Alberta, Canada, using measures of network centrality to determine the association between risk of infection of network members and their position within the sexual network. We also compared risk in smaller disconnected components with a large network centred on a social venue. METHODS: During the investigation of the outbreak, epidemiological data were collected on gonorrhoea cases and their sexual contacts from STI surveillance records. In addition to traditional contact tracing information, subjects were interviewed about social venues they attended in the past year where casual sexual partnering may have occurred. Sexual networks were constructed by linking together named partners. Univariate comparisons of individual network member characteristics and algebraic measures of network centrality were completed. RESULTS: The sexual networks consisted of 182 individuals, of whom 107 were index cases with laboratory confirmed gonorrhoea and 75 partners of index cases. People who had significantly higher information centrality within each of their local networks were found to have patronised a popular motel bar in the main town in the region (p = 0.05). When the social interaction through the bar was considered, a large network of 89 individuals was constructed that joined all eight of the largest local networks. Moreover, several networks from different communities were linked by individuals who served as bridge populations as a result of their sexual partnering. CONCLUSION: Asking clients about particular social venues emphasised the importance of location in disease transmission. Network measures of centrality, particularly information centrality, allowed the identification of key individuals through whom infection could be channelled into local networks. Such individuals would be ideal targets for increased interventions.

Adolescent↗
PubMed2000

Quantification of plasma lipids and apolipoproteins by use of proton NMR spectroscopy, multivariate and neural network analysis.

New approaches for quantification of human blood plasma lipids and apolipoproteins are presented. One method is based on multivariate analysis of proton nuclear magnetic resonance spectra of human blood plasma. Although similar approaches have been developed previously, this is the first time principal component analysis (PCA) and partial least squares regression (PLS) have been applied to this particular task. Further, a large proportion of the subjects in this study were cancer patients undergoing treatment, which introduced a new dimension to the quantification of lipoprotein distributions. Calibration models for prediction of lipids and apolipoproteins were constructed by use of PLS, and blind samples were used to test the predictive ability. Comparison of the predicted vs observed data obtained by standard clinical chemical procedures gave good agreement; the correlation coefficient for total plasma triglyceride was 0.99, for total plasma cholesterol 0.98, for LDL cholesterol 0. 97, and for HDL cholesterol 0.88. These results are comparable with those obtained with other methods. The quantitative analysis of 14 components (including total cholesterol and total triglyceride) of human blood plasma was also undertaken using various neural network (NN) analyses of selected portions of the spectra. Conventional fully connected backpropagation neural network topologies were capable of providing excellent predictions for the majority of the variables, confirming and reinforcing literature related to this approach. However HDL triglycerides were poorly predicted, while intermediate-quality results were obtained for the LDL cholesterol, plasma apoA1 and LDL apoB variables. In these instances, applying significantly different neural network algorithms involving either general regression or polynomial neural networks in combination with genetic adaptive components for parameter optimisation made improved predictions.

Adult↗
PubMed2006

An investigation into the usefulness of generalized regression neural network analysis in the development of level A in vitro-in vivo correlation.

Quantitative correlations between in vivo and in vitro data (IVIVC) reduce the number of human in vivo studies, thus decreasing the overall time and expenses necessary for the development of optimal drug product formulation. Although linear regression analysis represents the simplest relationship, it is recognized that IVIVC should not be limited to linear relationship. With regards to the implementation of non-linear IVIVC models and the ability of artificial neural network (ANN) computing to cope with non-linear relationships, the usefulness of ANN analysis in the development of IVIVC merits further evaluation. The present paper is an attempt to develop an IVIVC for model sustained release paracetamol matrix tablet formulations employing various correlation approaches based on linear and non-linear modeling of in vitro and in vivo data. Currently accepted compendial methodology was compared with the alternative approaches, involving general mixed effects model and generalized regression neural network (GRNN) analysis, in order to evaluate their usefulness for predicting the in vivo behavior of drug products. Although based on analogous approaches, data generated by GRNN were closer to those observed in vivo, leading to the higher level of IVIVC than obtained by convolution. It can be assumed that GRNN analysis was able to generalize complex relations between the output and input parameters and could account for the differences in drug release kinetics observed under various conditions in vitro, thus offering potential as a reliable and robust estimate of drug products in vivo behavior.

Acetaminophen↗
PubMed2000

Neural network analysis of oxygenation signals in infants during sleep.

The use of artificial neural networks (ANNs) to interpret sleep monitoring signals is described. Recordings from ten infants with apparent life threatening episodes were assigned into training feedforward R-PROP networks. In order to separate good signal from artefact, 60 second time frames of SaO2 and TcPO2 signals were processed and the mean and standard deviation values were used as inputs to the networks. Intra-human errors were minimized using this method whilst inter-human errors remained significant. To decrease the latter, the number of hidden units was increased to eight. Sensitivity figures of the SaO2 network were 0.93 and 0.9 for the training and test sets respectively whilst the specificity figures were 0.7 and 0.65 respectively. For the TcPO2 signals the above figures were 0.92, 0.85, 0.77 and 0.61 respectively.

Apnea↗
PubMed2006

A network analysis of changes in molecular interactions in cellular signaling.

Multiprotein complexes play an essential role in the propagation and integration of cellular signals. However, systems level analyses of signaling-dependent changes in the pattern of molecular interactions are still missing. Signaling in T-lymphocytes is one prominent example in which multiprotein complexes orchestrate signal transduction. We implemented peptide microarrays comprising a set of interaction motifs of signaling proteins for network-based analyses of signaling-dependent changes in molecular interactions. Lysates of resting or stimulated cells were incubated on these arrays, and the binding of signaling proteins was detected by immunofluorescence. Signaling-dependent complex formation led to changes of signals on the microarrays in two ways. 1) Masking of a binding site of a signaling protein for a peptide on the array resulted in a signal decrease. 2) Interaction of a protein with a second protein, which in turn binds to a peptide on the array, resulted in a signal increase for the first protein. Dissipation of complexes led to the reverse changes. Competition with peptides corresponding to interaction motifs provided detailed information on the architecture of complexes; lack of individual signaling proteins revealed the functional interdependence of interactions in the network. We show that complex formation through phosphorylation of the scaffolding protein LAT (linker for activation of T-cells) acted as a signal amplifier. PLCgamma1 deficiency increased the resting state levels of LAT-dependent complexes and augmented the recruitment of the phosphatase SHPTP2 into complexes. For the analysis of signaling networks, the parallel detection of changes in interactions enabled the identification of functional interdependencies with minimum a priori knowledge.

Adaptor Proteins, Signal Transducing↗
PubMed1991

Neural network analysis of serial cardiac enzyme data. A clinical application of artificial machine intelligence.

There has been a recent resurgence of interest in the study and application of computerized neural networks within the broad field of artificial intelligence. These "intelligent machines" are modeled after biological nervous systems and are fundamentally different from the many computerized expert systems that previously have been introduced as clinical decision-making aids. The authors describe a neural network designed and trained to predict the probability of acute myocardial infarction (AMI) based on the analysis of paired sets of cardiac enzymes. The neural network predicted 24 of 24 (100%) AMIs and 27 of 29 (93%) No-AMIs when compared with a pathologist's interpretation of the patient's laboratory data (P less than 0.000001). The authors attempted to validate the network's diagnoses by two independent methods. When compared with echocardiogram and EKG for diagnosis of AMI, the neural network agreed with the cardiologist's interpretation in 12 of 14 (86%) AMIs and 1 of 3 (33%) No-AMIs, but the correlation was not statistically significant. Using autopsy outcome for validation, the neural network agreed with the anatomic evidence in 24 of 26 (92%) AMIs and 4 of 6 (67%) No-AMIs (P = 0.001). The authors conclude that neural networks can be successfully applied to the analysis of cardiac enzyme data and suggest that broader applications exist within the domain of clinical decision support.

Amyloidosis↗
PubMed2004

Prediction of the affinity of the newly synthesised azapirone derivatives for 5-HT1A receptors based on artificial neural network analysis of chromatographic retention data and calculation chemistry parameters.

The performance of artificial neural network (ANN) in predicting the affinity of a series of 65 new azapirone derivatives for rat brain serotonin 5-HT1A receptors based on high-performance liquid chromatography (HPLC) retention data and on non-empirical structural parameters of the compounds' was studied. Affinity of the agents for rat brain 5-HT1A receptors were assessed in vitro and expressed as inhibitor constant values, Ki. The retention parameters determined in 14 HPLC systems along with compounds' structural descriptors from calculation chemistry were considered in ANN analysis. Supervised method of ANN learning with back-propagation strategy was used in ANN calculations. Two models of ANN of similar architecture were designed: the first one for the data based on chromatographic retention data and the second based on structural parameters of the agents. Each ANN model was trained with the data of training set. It was next used to classify the agents from the testing set into two groups: active (Ki < 50 nM) and inactive compounds (Ki > 50 nM). A high prediction performance of both ANN models considered as regards the affinity of new azapirone derivatives for the serotonin 5-HT1A receptors was demonstrated. However, the percent of correctly classified compounds was higher in the case of the ANN processing of the non-empirical structural descriptors of azapirone derivatives. Since the ANN analysis of the retention data and of the structural parameters originating from calculation chemistry allows to segregate drug candidates according to their pharmacological properties that, in consequence, may be of help to limit the number of biological assays in the search for new drugs.

Aza Compounds↗
PubMed1992

Network analysis of intermediary metabolism using linear optimization. II. Interpretation of hybridoma cell metabolism.

The reaction network of intermediary metabolism in the mammalian cell has been studied using linear optimization. Experimental measurements of metabolite fluxes entering and leaving hybridoma cell line 167.4G5.3 have been used to interpret the interactions of nutrients and the demand for intermediates for growth. We have ascertained the effects of waste production and energy loads on the cell growth rate using linear optimization. This analysis has shown that neither the maintenance demand for ATP nor the antibody production rate limit growth rate at normal experimental conditions. In addition, the cell uses its nutrients for growth with only 57-78% efficiency, due to the large secretion of alanine. The sensitivity of the growth rate with respect to the demand for cofactors and the supply of nutrients is given by the shadow price for each constraint. The shadow prices have shown that amino acids are the limiting nutrients at experimental conditions. The sensitivities of the growth rate to flux through reactions, given by the reduced costs, have shown that flux through the reaction glutamate dehydrogenase may actually slow down cell growth. We have also found that intermediates with lower shadow prices, and thus with lower value to the cell, are the precursors to compounds secreted from the cell. The shadow prices are also a means for comparing the costs of synthesizing various intermediates in terms of the two major nutrients, glucose and glutamine. At anaerobic conditions, glucose and glutamine have similar values to the cell, and the cost to synthesize most intermediates in terms of glucose is identical to the cost in terms of glutamine. At aerobic conditions, glucose is nearly twice as valuable to the cell as glutamine.

Adenosine Triphosphate↗
PubMed2005

Use of reverse phase protein microarrays and reference standard development for molecular network analysis of metastatic ovarian carcinoma.

Cancer can be defined as a deregulation or hyperactivity in the ongoing network of intracellular and extracellular signaling events. Reverse phase protein microarray technology may offer a new opportunity to measure and profile these signaling pathways, providing data on post-translational phosphorylation events not obtainable by gene microarray analysis. Treatment of ovarian epithelial carcinoma almost always takes place in a metastatic setting since unfortunately the disease is often not detected until later stages. Thus, in addition to elucidation of the molecular network within a tumor specimen, critical questions are to what extent do signaling changes occur upon metastasis and are there common pathway elements that arise in the metastatic microenvironment. For individualized combinatorial therapy, ideal therapeutic selection based on proteomic mapping of phosphorylation end points may require evaluation of the patient's metastatic tissue. Extending these findings to the bedside will require the development of optimized protocols and reference standards. We have developed a reference standard based on a mixture of phosphorylated peptides to begin to address this challenge.

Carcinoma↗
PubMed2004

Wavelet-based neural network analysis of ophthalmic artery Doppler signals.

In this study, ophthalmic artery Doppler signals were recorded from 115 subjects, 52 of whom had ophthalmic artery stenosis while the rest were healthy controls. Results were classified using a wavelet-based neural network. The wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of ophthalmic artery Doppler signals. A multilayer perceptron neural network (MLPNN) trained with the Levenberg-Marquardt algorithm was used to detect stenosis in ophthalmic arteries. In order to determine the MLPNN inputs, spectral analysis of ophthalmic artery Doppler signals was performed using wavelet transform. The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively. All data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ophthalmic artery stenosis. The correct classification rate was 97.22% for healthy subjects, and 96.77% for subjects having ophthalmic artery stenosis. The classification results showed that the MLPNN trained with the Levenberg-Marquardt algorithm was effective to detect ophthalmic artery stenosis.

Adult↗
PubMed2004

Network analysis of metabolic enzyme evolution in Escherichia coli.

BACKGROUND: The two most common models for the evolution of metabolism are the patchwork evolution model, where enzymes are thought to diverge from broad to narrow substrate specificity, and the retrograde evolution model, according to which enzymes evolve in response to substrate depletion. Analysis of the distribution of homologous enzyme pairs in the metabolic network can shed light on the respective importance of the two models. We here investigate the evolution of the metabolism in E. coli viewed as a single network using EcoCyc. RESULTS: Sequence comparison between all enzyme pairs was performed and the minimal path length (MPL) between all enzyme pairs was determined. We find a strong over-representation of homologous enzymes at MPL 1. We show that the functionally similar and functionally undetermined enzyme pairs are responsible for most of the over-representation of homologous enzyme pairs at MPL 1. CONCLUSIONS: The retrograde evolution model predicts that homologous enzymes pairs are at short metabolic distances from each other. In general agreement with previous studies we find that homologous enzymes occur close to each other in the network more often than expected by chance, which lends some support to the retrograde evolution model. However, we show that the homologous enzyme pairs which may have evolved through retrograde evolution, namely the pairs that are functionally dissimilar, show a weaker over-representation at MPL 1 than the functionally similar enzyme pairs. Our study indicates that, while the retrograde evolution model may have played a small part, the patchwork evolution model is the predominant process of metabolic enzyme evolution.

Algorithms↗
PubMed1989

Supportive and problematic social interactions: a social network analysis.

Assessed the number of sources of supportive and problematic social interactions in the total social network as well as the number who were the sources of consistently supportive or problematic interactions in a sample of 2nd year medical students. Number of problematic interaction sources was hypothesized to be more predictive of psychological and physical well-being than number of supportive interaction sources. Results showed that total supportive and total problematic interaction sources were equally predictive of life satisfaction. The presence of at least one individual who is a consistent source of problematic interactions was most predictive of lower life satisfaction. Results indicate the importance of studying both supportive and problematic social interactions. They also suggest that interactions with individuals that are consistently problematic may negatively impact upon well-being.

Adaptation, Psychological↗