Lipoprotein-lipid quantification by neural-network analysis of 1H NMR data from human blood plasma.
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Cholesterol↗
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Network analysis of Danish cattle industry trade patterns as an evaluation of risk potential for disease spread.
Trade patterns of animal movements in a specific industry are complex and difficult to study because there are many stakeholders, premises that are heterogeneously spread over the country, and a highly dynamic flow of animals exists among them. The Danish cattle industry was defined as a network of animal movements and graph theory was used to analyse the movements of cattle within this network. A premise was defined as a farm, an abattoir or a market. These premises constituted the network nodes in the graph and the animal movements between them were the links. In this framework, each premise had a sub-network of other premises to which it was linked by these animal movements. If no movement of animals were registered for a specific farm, then the sub-network for that premise consisted of only that premise. Otherwise, the sub-network linked the premise of interest to all premises from which and to which animals were moved, as long as there was a path linking animal movements to that specific premise. This approach allowed visualization and analyses of four levels of organization that existed in Denmark animal registers: (1) the animal that was moved, (2) the movements of all animals between two premises, (3) the specific premise network, and (4) the overall industry network. When contagious animals are moved from one premise to another, then to a third and so forth, these movements create a path for potential transfer of pathogens. The paths within which pathogens are present identify the transmission risks. A network of animal movements should provide information about pathogen transmission and disease spread. The network of the Danish cattle industry network was a directed scale-free graph (the direction of a movement was known), with an in-degree power of 2 an out-degree power of 1.46, consisted of 29,999 nodes, and 130,265 movements during a 6-month period. The in clustering coefficient was calculated to be 0.52 for the inward direction (movement to), while it was 0.02 for the outward direction (movement from). In Denmark, the cattle movements between premises demonstrated a large degree of heterogeneity. This heterogeneity in movements between farms should be used to evaluate the risk potential of disease transmission for each premise and must be considered when modelling disease spread between premises. The objective of this research was to describe the network of animal movements and not just the animal movements per se.
Animals↗
Neural network analysis of flow cytometric data for 40 marine phytoplankton species.
Flow cytometry data (time of flight, horizontal and vertical forward light scatter, 90 degrees light scatter, and "red" and "orange" integral fluorescence) were collected for laboratory cultures of 40 species of marine phytoplankton, from the following taxonomic classes, the Dinophyceae, Bacillariophyceae, Prymnesiophyceae, Cryptophyceae, and other flagellates. Single-hidden-layer "back-propagation" neural networks were trained to discriminate between species by recognising patterns in their flow cytometric signatures, and network performance was assessed using an independent test data set. Two approaches were adopted employing: (1) a hierarchy of small networks, the first identifying to which major taxonomic group a cell belonged, and then a network for that taxonomic group identified to species, and (2) a single large network. Discriminating some of the major taxonomic groups was successful but others less so. With networks for specific groups, cryptophyte species were all identified reliably (probability of correct classification always being > 0.75); in the other groups half of the species were identified reliably. With the large network, dinoflagellates, cryptomonads, and flagellates were identified almost as well as by networks specific for these groups. The application of neural computing techniques to identification of such a large number of species represents a significant advance from earlier studies, although further development is required.
Flow Cytometry↗
Detection of characteristic waves of sleep EEG by neural network analysis.
In psychiatry, the sleep stage is one of the most important evidence for diagnosing mental disease. However, doctors require much labor and skill for diagnosis, so a quantitative and objective method is required for more accurate diagnosis since it depends on the doctor's experience. For this reason, an automatic diagnosis system must be developed. In this paper, we propose a new type of neural network (NN) model referred to as a sleep electroencephalogram (EEG) recognition neural network (SRNN) which enables us to detect several kinds of important characteristic waves in sleep EEG which are necessary for diagnosing sleep stages. Experimental results indicate that the proposed NN model was much more capable than other conventional methods for detecting characteristic waves.
Adult↗
Metabolic network analysis of the causes and evolution of enzyme dispensability in yeast.
Under laboratory conditions 80% of yeast genes seem not to be essential for viability. This raises the question of what the mechanistic basis for dispensability is, and whether it is the result of selection for buffering or an incidental side product. Here we analyse these issues using an in silico flux model of the yeast metabolic network. The model correctly predicts the knockout fitness effects in 88% of the genes studied and in vivo fluxes. Dispensable genes might be important, but under conditions not yet examined in the laboratory. Our model indicates that this is the dominant explanation for apparent dispensability, accounting for 37-68% of dispensable genes, whereas 15-28% of them are compensated by a duplicate, and only 4-17% are buffered by metabolic network flux reorganization. For over one-half of those not important under nutrient-rich conditions, we can predict conditions when they will be important. As expected, such condition-specific genes have a more restricted phylogenetic distribution. Gene duplicates catalysing the same reaction are not more common for indispensable reactions, suggesting that the reason for their retention is not to provide compensation. Instead their presence is better explained by selection for high enzymatic flux.
Biomass↗
Prediction of significant fibrosis in hepatitis C virus infected liver transplant recipients by artificial neural network analysis of clinical factors.
OBJECTIVES: Interest in developing noninvasive markers of liver fibrosis continues to increase, especially in recurrent hepatitis C virus infection after liver transplantation. Recently, a model for predicting significant fibrosis (bridging fibrosis and cirrhosis) on the basis of logistic regression and routine laboratory data has been proposed (logit model). The aim of the present study was to evaluate the accuracy of an artificial neural network, a technique reported to work better than logit models in complex biological situations, built on those same clinical variables and data set of patients, in predicting significant fibrosis. METHODS: The neural network was constructed on the training set of 414 protocol biopsies, from liver transplant recipients, and then tested on the remaining 96 biopsies, as validation set. Model performances of neural network and logit model were evaluated and compared by means of areas under receiver operating characteristic curves. RESULTS: With a cutoff value of >0.4 to predict significant fibrosis, the neural network provided sensitivity, specificity, positive and negative predictive values, respectively, of 100, 79.5, 60.5 and 100%, in the validation set. The performance of the neural network was significantly better than that of the logit model (in the validation set area under the curve = 0.93 vs. 0.84; P = 0.045). CONCLUSIONS: Artificial neural network provides accurate prediction of the presence or absence of significant fibrosis from clinical variables, allowing theoretically protocol liver biopsy to be avoided in several instances, a result of particular interest, given the lack of other types of reliable noninvasive indexes of fibrosis in the setting of transplantation.
Adult↗
Positron emission tomography of 5-HT reuptake sites in the human brain with C-11 McN5652 extraction of characteristic images by artificial neural network analysis.
The goal of this work was to identify the distribution of serotonin transporters in the human brain with [11C](+)McN5652/PET. Four healthy volunteers were studied. To determine non-specific binding, a PET study was also performed with the inactive enantiomer [11C](-)McN5652 as well as with [11C](+)McN5652 after pretreatment with fluoxetine. For pattern extraction the PET data sets were analyzed by a back-propagation neural network. Two pharmacokinetic patterns and two characteristic images were separated; one representing specific binding, the other representing non-specific binding. The specific binding image showed characteristic distribution of serotonin transporters with [11C](+)McN5652. The pattern images demonstrated an improvement in image quality compared to the original PET images (reduced variance, higher region-to-cerebellum ratio, good correlation with known density of serotonin transporters). The non-specific binding images extracted from [11C](-)McN5652/PET were similar to those of [11C](-)McN5652 and [11C](-)McN5652 with fluoxetine. Thus, PET studies obtained with [11C](+)McN5652 largely represent the regional distribution of the serotonin transporters and the inactive enantiomer [11C](-)McN5652 shows the distribution of its nonspecific binding.
Adult↗
[C-terminal fragment of tetanus toxin: its use in neuronal network analysis and its potential as non-viral vector].
The atoxic C-terminal fragment of tetanus neurotoxin or TTC fragment presents similar retrograde and transsynaptic properties to that of holotoxin. Detection of this fragment is easier when it is associated with a fluorescent marker or with beta-galactosidase activity by genetic fusion or chemical conjugation. Thus, these tracers have been used to study and analyse the synaptic connections of a neural network. In this article, we shortly review the various methods used with this aim including: injection of the fusion protein, adenovirus in vivo expression and transgenesis. Since neural activity is essential for neuronal TTC binding and internalization, the functionality of connections can be also evaluated. Moreover, modifications of the retrograde transport can be detected by using this fragment. Thus, TTC fragment is an excellent tracer to analyse the connectivity and functionality of a neural network. The TTC fragment was also soon proposed as potential therapeutic vector to transport and to deliver a biological activity or gene in a neural network. With this aim, the efficiency of a translocation domain to induce the cytosolic release of the associated activity has been evaluated. The use of the TTC fragment to target specifically a neurotrophic factor to neurons and thus avoid secondary effects has been tested with interesting results.
Animals↗
Mycobacterium tuberculosis functional network analysis by global subcellular protein profiling.
Trends in increased tuberculosis infection and a fatality rate of approximately 23% have necessitated the search for alternative biomarkers using newly developed postgenomic approaches. Here we provide a systematic analysis of Mycobacterium tuberculosis (Mtb) by directly profiling its gene products. This analysis combines high-throughput proteomics and computational approaches to elucidate the globally expressed complements of the three subcellular compartments (the cell wall, membrane, and cytosol) of Mtb. We report the identifications of 1044 proteins and their corresponding localizations in these compartments. Genome-based computational and metabolic pathways analyses were performed and integrated with proteomics data to reconstruct response networks. From the reconstructed response networks for fatty acid degradation and lipid biosynthesis pathways in Mtb, we identified proteins whose involvements in these pathways were not previously suspected. Furthermore, the subcellular localizations of these expressed proteins provide interesting insights into the compartmentalization of these pathways, which appear to traverse from cell wall to cytoplasm. Results of this large-scale subcellular proteome profile of Mtb have confirmed and validated the computational network hypothesis that functionally related proteins work together in larger organizational structures.
Automation↗
Using artificial neural network analysis of global ventilation-perfusion scan morphometry as a diagnostic tool.
OBJECTIVE: The purpose of this study was to determine whether global statistical data from radionuclide ventilation-perfusion scans could predict the likelihood of pulmonary embolism. MATERIALS AND METHODS: Digital data were obtained from 161 patients undergoing both radionuclide ventilation-perfusion scanning and subsequent pulmonary angiography. Morphometric data characterizing whole-lung perfusion and ventilation parameters were input into artificial neural networks in an attempt to predict the likelihood of pulmonary embolism. RESULTS: The performance of artificial neural networks using only automated global region of interest-based data was superior to that of clinicians in predicting the likelihood of acute pulmonary embolism in patients with normal findings on chest radiographs with segmental or larger emboli (p < .005) and in patients with normal findings on chest radiographs and emboli of any size (p < .01). Network performance did not significantly differ from clinician performance in patients with abnormal findings on chest radiographs. CONCLUSION: The adjunctive use of artificial neural networks using only user-independent, standard image statistics can significantly improve accuracy in the diagnosis of pulmonary embolism in patients with normal findings on chest radiographs.
Humans↗
Classification of epilepsy types through global network analysis of scalp electroencephalograms.
Epilepsy is a dynamic disease in which self-organization and emergent structures occur dynamically at multiple levels of neuronal integration. Therefore, the transient relationship within multichannel electroencephalograms (EEGs) is crucial for understanding epileptic processes. In this paper, we show that the global relationship within multichannel EEGs provides us with more useful information in classifying two different epilepsy types than pairwise relationships such as cross correlation. To demonstrate this, we determine the global network structure within channels of the scalp EEG based on the minimum spanning tree method. The topological dissimilarity of the network structures from different types of temporal lobe epilepsy is described in the form of the divergence rate and is computed for 11 patients with left (LTLE) and right temporal lobe epilepsy (RTLE). We find that patients with LTLE and RTLE exhibit different large scale network structures, which emerge at the epoch immediately before the seizure onset, not in the preceding epochs. Our results suggest that patients with the two different epilepsy types display distinct large scale dynamical networks with characteristic epileptic network structures.
Artificial Intelligence↗
Network analysis of brain cognitive function using metabolic and blood flow data.
Functional neuroimaging has become a powerful tool for investigating the neurobiological foundations of cognition. An overview is presented of the two major strategies by which such data are currently analyzed. One strategy compares the pattern of activity between two (or more) tasks, looking for those brain areas that show significant changes. The second investigates the functional relationships between regional activities in an attempt to determine the systems-level neural networks mediating the tasks. Object and spatial visual processing tasks are used to illustrate each of these strategies.
Animals↗
Reduced-median-network analysis of complete mitochondrial DNA coding-region sequences for the major African, Asian, and European haplogroups.
The evolution of the human mitochondrial genome is characterized by the emergence of ethnically distinct lineages or haplogroups. Nine European, seven Asian (including Native American), and three African mitochondrial DNA (mtDNA) haplogroups have been identified previously on the basis of the presence or absence of a relatively small number of restriction-enzyme recognition sites or on the basis of nucleotide sequences of the D-loop region. We have used reduced-median-network approaches to analyze 560 complete European, Asian, and African mtDNA coding-region sequences from unrelated individuals to develop a more complete understanding of sequence diversity both within and between haplogroups. A total of 497 haplogroup-associated polymorphisms were identified, 323 (65%) of which were associated with one haplogroup and 174 (35%) of which were associated with two or more haplogroups. Approximately one-half of these polymorphisms are reported for the first time here. Our results confirm and substantially extend the phylogenetic relationships among mitochondrial genomes described elsewhere from the major human ethnic groups. Another important result is that there were numerous instances both of parallel mutations at the same site and of reversion (i.e., homoplasy). It is likely that homoplasy in the coding region will confound evolutionary analysis of small sequence sets. By a linkage-disequilibrium approach, additional evidence for the absence of human mtDNA recombination is presented here.
Africa↗
Multi-layer neural network analysis of cerebrospinal fluid pressure patterns in idiopathic normal-pressure hydrocephalus.
The cerebrospinal fluid (CSF) pressure patterns have been reported as one of the most relevant indexes for the diagnosis and treatment of idiopathic normal-pressure hydrocephalus (INPH). Forty consecutive patients coming from our observations with the classic Hakim's triad underwent continuous CSF pressure monitoring via lumbar puncture for at least 12 hours. Twenty-eight patients were diagnosed as having INPH and underwent CSF shunt. A multi-layer neural network (perceptron) was employed to study the pressure patterns in order to try an alternative classification to the "expert" neurosurgeon one. Differences between expert and neural network classifications were indeed observed. Such differences may depend on the small group studied or on the inadequacy of CFS pressure patterns in correctly individuating those INPH patients who benefit from shunt surgery. The authors think that neural network processing of INPH could add relevant information to select the "responder" patients to surgery: in fact neural networks represent a powerful methodology for aiding the expert to select the proper choice on the basis of "what learnt" by the networks themselves.
Cerebrospinal Fluid↗
Artificial neural network analysis: a novel application for predicting site-specific bone mineral density.
Dual X-ray absorptiometry (DXA), which is the most commonly used method for the diagnosis and followup of human bone health, is known to produce accurate estimates of bone mineral density (BMD). However, high costs and problems with availability may prevent its use for mass screening. The objective of the present study was to estimate BMD values for healthy persons and those with conditions known to be associated with BMD, using artificial neural networks (ANN). An ANN was used to quantitatively estimate site-specific BMD values in comparison with reference values obtained by DXA (i. e. BMD(spine), BMD(pelvis), and BMD(total)). Anthropometric measurements (i. e. sex, age, weight, height, body mass index, waist-to-hip ratio, and the sum of four skinfold thicknesses) were fed to the ANN as independent input variables. The estimates based on four input variables were generated as output and were generally identical to the reference values for all studied groups. We believe the ANN is a promising approach for estimating and predicting site-specific BMD values using simple anthropometric measurements.
Bone Density↗
The 'geneticisation' of heart disease: a network analysis of the production of new genetic knowledge.
Genetic science is making ever-expanding claims about the (mal)functioning of the body. The 'geneticisation' of health and medicine is extending from rare single gene conditions to more common multi-factorial disease, such as heart disease. The dominant behavioural and socio-spatial explanations of heart disease are now being challenged by genetic claims of deterministic biological causes. This paper builds an account of the transformation of heart disease in the new genetics era, by applying actor network theory (ANT) to the production of genetic knowledge of one aspect of heart disease-hypertension-within a medical genetics laboratory in Glasgow, Scotland. Using this approach, the paper shows that there is no straightforward geneticisation of heart disease. Instead, there is a contested, complex and uncertain understanding of heart disease as genetic, a product of the many people, technologies, natural elements and spaces involved in the network of genetic science knowledge making. The paper concludes that a 'critical' ANT could be developed that acknowledges the inherent unevenness of the network, and connects genetic and socio-spatial explanations of heart disease.
Causality↗
Pathways of urothelial cancer progression suggested by Bayesian network analysis of allelotyping data.
Urothelial cancers of the bladder (UC) comprise biologically heterogeneous group of tumors and display complex genetic alterations. Several genetic changes have been analyzed in detail and some of them are associated with the development and progression of UCs. Only a few studies, however, are focused on identifying the order in which the aberrations may appear during UC tumorigenesis. We have analyzed 123 papillary UCs of the bladder by microsatellites for each of the chromosomal regions that have been suggested to be specifically involved in this type of tumor. We used Bayesian network modeling that enables to uncover multivariate probabilistic dependencies between variables. This methodology applied to LOH data allowed us to discover patterns of losses in UCs. Exploiting the mechanism of probabilistic reasoning in Bayesian networks we suggest primary and secondary events in tumor pathogenesis and reconstruct the possible flow of progression of allelic changes. Losses of chromosome 9p and 9q were found to be the primary events. Losses of 8p and 17p are important events leading to progression of tumor cell clones. The loss of 17p occurs when both abnormalities of chromosome 9 and 8p are already present. There are chromosomal losses related to 8p (1q, 18q, 10q) and some losses like 5q/5p were associated with 17p, leading to the hypothesis of different genetic pathways of UC progression. The abnormalities of chromosome regions 13q, 16q, 6q, 14q, 3p are suggested to be late events being accumulated during the progression of cancer. Although some genetic changes were associated only with the 8p pathway, most secondary genetic changes appear in both pathways. Supplementary material for this article can be found on the International Journal of Cancer website at http://www.interscience.wiley.com/jpages/0020-7136/suppmat/index.html.
Alleles↗
Metabolic network analysis on Phaffia rhodozyma yeast using 13C-labeled glucose and gas chromatography-mass spectrometry.
Carotenoid production by microorganisms, as opposed to chemical synthesis, could fulfill an ever-increasing demand for 'all natural' products. The yeast Phaffia rhodozyma has received considerable attention because it produces the red pigment astaxanthin, commonly used as an animal feed supplement. In order to have a better understanding of its metabolism, labeling experiments with [1-(13)C]glucose were conducted with the wildtype strain (CBS5905T) and a hyper-producing carotenoid strain (J4-3) in order to determine their metabolic network structure and estimate intracellular fluxes. Amino acid labeling patterns, as determined by GC-MS, were in accordance with a metabolic network consisting of the Embden-Meyerhof-Parnas pathway, the pentose phosphate pathway, and the TCA cycle. Glucose was mainly consumed along the pentose phosphate pathway ( approximately 65% for wildtype strain), which reflected high NADPH requirements for lipid biosynthesis. Although common to other oleaginous yeast, there was no, or very little, malic enzyme activity for carbon-limited growth. In addition, there was no evidence of phosphoketolase activity. The central carbon metabolism of the mutant strain was similar to that of the wildtype strain, though the relative pentose phosphate flux was lower and the TCA cycle flux in accordance with the biomass yield being lower.
Basidiomycota↗