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PubMed1996

Neural network analysis of the EMG interference pattern.

This paper investigates the performance of artificial neural networks for analysing and classifying EMG signals from healthy subjects and patients with myopathic and neuropathic disorders. EMG interference patterns (IP) were recorded under maximum voluntary contraction from the right biceps of a total of 50 subjects. Parameters were obtained from the signals using recognized quantification techniques including turns analysis, small segments analysis and frequency analysis. Supervised networks examined were an improved backpropagation network (IBPN), a radial basis network (RBN), and a learning vector quantization network (INQ). Supervised networks using different combinations of parameters from turns analysis and small segments analysis gave diagnostic yields of 60-80%. Combinations using frequency analysis parameters produced similar results. The performance of unsupervised Self-Organising Feature Maps (SOFM) was generally lower than that of the supervised networks. Including personal data (sex and age) did not improve the overall performance.

Biomedical Engineering↗
PubMedDate not supplied

Algorithms for network analysis in systems-ADME/Tox using the MetaCore and MetaDrug platforms.

The authors have previously applied two integrated platforms, MetaCore and MetaDrug, for the assembly and analysis of human biological networks as a useful method for the integration and functional interpretation of high-throughput experimental data. The present study demonstrates in detail the specific algorithms that are used in both software platforms. Using a standard set of genes as input, namely CYP3A4 (an enzyme), PXR (a nuclear hormone receptor), MDR1 (a transporter) and hERG (an ion channel) related to the absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) of xenobiotics, we have now generated networks with each algorithm. The relative advantages and disadvantages of these algorithms are explained using these examples as well as appropriate instances of utility to illustrate further the particular circumstances for their use. In addition, the benefits of the different network algorithms are identified when compared with algorithms available in other products, where this information is available.

Algorithms↗
PubMed2004

An improved algorithm for stoichiometric network analysis: theory and applications.

MOTIVATION: Genome scale analysis of the metabolic network of a microorganism is a major challenge in bioinformatics. The combinatorial explosion, which occurs during the construction of elementary fluxes (non-redundant pathways) requires sophisticated and efficient algorithms to tackle the problem. RESULTS: Mathematically, the calculation of elementary fluxes amounts to characterizing the space of solutions to a mixed system of linear equalities, given by the stoichiometry matrix, and linear inequalities, arising from the irreversibility of some or all of the reactions in the network. Previous approaches to this problem have iteratively solved for the equalities while satisfying the inequalities throughout the process. In an extension of previous work, here we consider the complementary approach and derive an algorithm which satisfies the inequalities one by one while staying in the space of solution of the equality constraints. Benchmarks on different subnetworks of the central carbon metabolism of Escherichia coli show that this new approach yields a significant reduction in the execution time of the calculation. This reduction arises since the odds that an intermediate elementary flux already fulfills an additional inequality are larger than when having to satisfy an additional equality constraint.

Algorithms↗
PubMed1993

Application of multivariate, fuzzy set and neural network analysis in quantitative cytological examinations.

Multivariate statistical methods have been used in several studies to increase the diagnostic reliability of TV image analyser systems. In recent years some algorithms for decision support (fuzzy logic) and for pattern recognition (neural nets), both non-linear, were developed. This paper reports on preliminary results obtained with these methods in quantitative cytology and compares them to the traditional classifiers. A total of 21 normal, 15 dysplastic and 23 malignant, gastric imprint smears were Feulgen stained and analysed on a Leitz Miamed DNA cytophotometer system. Mean DNA content, the 2c deviation index (2cDI), 5c exceeding rate (5cER), G1, S, G2 phase fraction ratios, cell nucleus area and form factor were determined. Diagnostic accuracy of the discriminant analysis was 96% for the malignant cases, 87% for dysplasias and 81% for normal cases. Cluster analysis gave no significant result. Our diagnostic system utilizing fuzzy logic has made the diagnostic borders adjustable and reliable. The back-propagation neural net correctly classified the normal and malignant cases (100%) and all but one of the dysplasias (98%). The non-linear mathematical methods improved the reliability of the diagnostic system. These new algorithms gave results comparable to traditional classifiers. The application of these methods to clinical samples is encouraging.

Algorithms↗
PubMed2006

The cognitive phenotype of Down syndrome: insights from intracellular network analysis.

Down syndrome (DS) is caused by trisomy of chromosome 21. All individuals with DS exhibit some level of cognitive dysfunction. It is generally accepted that these abnormalities are a result of the upregulation of genes encoded by chromosome 21. Many chromosome 21 proteins are known or predicted to function in critical neurological processes, but typically they function as modulators of these processes, not as key regulators. Thus, upregulation in DS is expected to cause only modest perturbations of normal processes. Systematic approaches such as intracellular network construction and analysis have not been generally applied in DS research. Networks can be assembled from high-throughput experiments or by text-mining of experimental literature. We survey some new developments in constructing such networks, focusing on newly developed network analysis methodologies. We propose how these methods could be integrated with creation and manipulation of mouse models of DS to advance our understanding of the perturbed cell signaling pathways in DS. This understanding could lead to potential therapeutics.

Animals↗
PubMed2006

Testing introgressive hybridization hypotheses using statistical network analysis of nuclear and cytoplasmic haplotypes in the leaf beetle Timarcha goettingensis species complex.

Previous studies of leaf beetles (Chrysomelidae) in the Timarcha goettingensis species complex using mitochondrial (cox2) and nuclear (ITS-2 rRNA) markers revealed two main clades confined to the Iberian Peninsula and the rest of Europe but showing incongruent distributions indicative of gene exchange between both groups. Because of the anastomosing nature of hybridization, which disrupts the cladistic structure of character variation, phylogenetic trees might be inappropriate to represent and study this process. Here we test for evidence of hybridization in the T. goettingensis complex by analyzing the extra homoplasy arising in hybrid genomes from the simultaneous analysis of genetically independent markers. Haplotype networks obtained by Templeton's statistical parsimony analysis were generated for combined (concatenated) cox2 and ITS-2 sequences from 167 individuals of the T. goettingensis complex. Networks were used to detect runs of homoplasious characters physically clustered along a nucleotide sequence, as evidence for recombination between both gene partitions. A hypergeometric tail probability for the chance occurrence of physically clustered character changes on the connections linking networks of genotypes was applied. The test recognized two instances of statistically significant clustering, indicating the presence of cox2-ITS-2 mosaic genotypes and reticulation of both main T. goettingensis clades, supporting the reticulate origin of samples of T. maritima in southwestern France and T. sinuatocollis/T. monserratensis in the eastern Pyrenees. Although the assessment of reticulation in DNA sequences does not provide direct proof for hybridization, the geographical distribution of mosaic genotypes in the vicinity of "pure" genotypes supports the effect of gene flow between the two divergent lineages. The study demonstrates the utility of statistical parsimony networks for the detection of hybrids in the growing number of phylogeographic studies based on multiple gene markers.

Adaptor Proteins, Vesicular Transport↗
PubMed2005

Functional network analysis reveals extended gliomagenesis pathway maps and three novel MYC-interacting genes in human gliomas.

Gene expression profiling has proven useful in subclassification and outcome prognostication for human glial brain tumors. The analysis of biological significance of the hundreds or thousands of alterations in gene expression found in genomic profiling remains a major challenge. Moreover, it is increasingly evident that genes do not act as individual units but collaborate in overlapping networks, the deregulation of which is a hallmark of cancer. Thus, we have here applied refined network knowledge to the analysis of key functions and pathways associated with gliomagenesis in a set of 50 human gliomas of various histogenesis, using cDNA microarrays, inferential and descriptive statistics, and dynamic mapping of gene expression data into a functional annotation database. Highest-significance networks were assembled around the myc oncogene in gliomagenesis and around the integrin signaling pathway in the glioblastoma subtype, which is paradigmatic for its strong migratory and invasive behavior. Three novel MYC-interacting genes (UBE2C, EMP1, and FBXW7) with cancer-related functions were identified as network constituents differentially expressed in gliomas, as was CD151 as a new component of a network that mediates glioblastoma cell invasion. Complementary, unsupervised relevance network analysis showed a conserved self-organization of modules of interconnected genes with functions in cell cycle regulation in human gliomas. This approach has extended existing knowledge about the organizational pattern of gene expression in human gliomas and identified potential novel targets for future therapeutic development.

Adult↗
PubMed2004

Artificial neural network analysis for evaluation of peptide MS/MS spectra in proteomics.

The aim of the work was to explore usefulness of artificial neural network (ANN) analysis for the evaluation of proteomics data. The analysis was applied to the data generated by the widely used protein identification program Sequest, completed with several structural parameters readily calculated from peptide molecular formulas. Proteins from yeast cells were identified based on the MS/MS spectra of peptides. The constructed ANN was demonstrated to classify automatically as either "good" or "bad" the peptide MS/MS spectra otherwise classified manually. An appropriately trained ANN proves to be a high-throughput tool facilitating examination of Sequest's results. ANNs are recommended as a means of automatic processing of large amounts of MS/MS data, which normally must be considered in the analysis of complex mixtures of proteins in proteomics.

Artificial Intelligence↗
PubMed2003

Geometrical capillary network analysis.

BACKGROUND: Skin microcirculation, especially the superficial network, can be assessed by a computer capillary video microscope system. The study of morphology and dynamics of microcirculation must include all dynamic and cooperative processes between the capillaries. For characterizing capillary ensembles, the statistical and geometrical properties of the network need to be explored. METHODS: The microvaculature of the skin and the microcirculation were investigated by combining videocapillaroscopy (VCP) and image processing techniques based on computational geometry and graph theory. Our goal was to characterize the capillary network in noisy pictures of the scalp. Different geometric methods were developed, based on proximity parameters (distance and surface) in order to circumscribe and construct this network. RESULTS: By studying the distribution of these parameters, extreme values or outliers, which usually correspond to artifact subregions in the pictures could be eliminated. Different algorithms were developed and has been implemented in an image processing software (Capilab Toolbox). CONCLUSION: This computerized system is capable of real-time processings, increasing the quality of videocapillaroscope images and minimizing the disturbance of artifacts. The algorithms presented here are easy to implement and can process any kind of images of the skin, even in the scalp. In association with an example-based detection system, this method can be generalized to other stimuli in the same conditions.

Capillaries↗
PubMed1993

Advanced ovarian cancer. Neural network analysis to predict treatment outcome.

BACKGROUND: Quantitative methods for the analysis of prognostic information are important in order to use this knowledge optimally. The neural network is a new quantitative method where the fundamental building blocks are units which can be likened to neurons, and weighted connections which can be likened to synapses. The more the hidden units, the more complex the patterns that can be learnt. MATERIALS AND METHODS: Data from two Dutch studies in ovarian cancer were used to compare the previously reported survival rates predicted by the Cox's prognostic index with the prediction obtained by a neural network. RESULTS: Both the Cox's analysis and the neural network agreed on residual tumour size, stage, and performance status as being important for survival. The neural network identified additional predictive factors such as place of diagnosis and age. As the Cox's prognostic index has not been tested to predict survival on an independent data set a comparison with the results obtained in the neural network test set could not be performed. CONCLUSIONS: Neural networks perform at least as well as Cox's method for the prediction of survival, and prognostic factors can easily be identified. The analysis not only revealed the predictive power of some characteristics, but also the non-predictive power of the others.

Antineoplastic Combined Chemotherapy Protocols↗
PubMed1995

Resolution of batch variations in pyrolysis mass spectrometry of bacteria by the use of artificial neural network analysis.

A simple, but stringent, three group model of bacterial interstrain identity (two cultures of the same strain of Escherichia coli) and difference (a culture of a serologically distinct strain) was used in multiple serial weekly subcultures for five weeks to demonstrate the effect of both growth-related (phenotypic) and machine-related variation on pyrolysis mass spectra. An aliquot of serum from a single sample was included in each pyrolysis batch to distinguish machine drift from culture drift. Conventional principal component (PC) canonical variate (CV) analysis was successful within each pyrolysis batch but the variations between batches precluded the use of data from more than one batch in successful PCCV analysis. In contrast, artificial neural networks (ANNs) trained with data from one batch could be successfully used to identify groups in data from non-contemporaneous pyrolysis batches. Although the ANN method will require validation in more complex settings than this simple model, it is a promising approach to the problem of batch constraint in pyrolysis mass spectrometry.

Bacterial Typing Techniques↗
PubMed2006

Genomic cluster and network analysis for predictive screening for hepatotoxicity.

The present study was undertaken to estimate the usefulness of genomic approaches to predict hepatotoxicity. Male rats were treated with acetaminophen (APAP), carbon tetrachloride (CCL), amiodarone (AD) or tetracycline (TC) at toxic doses. Their livers were extracted 6 or 24 hr after the dosings and were used for subsequent examinations. At 6 hr there were no histological changes noted in any of the groups except for the CCL group, but at 24 hr, such changes were noted in all but the AD group. Regarding genomic analysis, we performed hierarchical cluster analysis using S-plus software. The individual microarray data were clearly classified into 5 treatment-related clusters at 24 hr as well as at 6 hr, even though no morphological changes were noted at 6 hr. In the gene expression analysis using GeneSpring, transcription factor and oxidative stress- and lipid metabolism-related genes were markedly affected in all treatment groups at both time points when compared with the corresponding control values. Finally, we investigated gene networks in the above-affected genes by using Ingenuity Pathway Analysis software. Down-regulation of lipid metabolism-related genes regulated by SREBP1 was observed in all treatment groups at both time points, and up-regulation of oxidative stress-related genes regulated by Nrf2 was observed in the APAP and CCL treatment groups. From the above findings, for the application of genomic approaches to predict hepatotoxicity, we considered that cluster analysis for classification and early prediction of hepatotoxicity and network analysis for investigation of toxicological biomarkers would be useful.

Acetaminophen↗
PubMed2006

[Impact on the quality of pathology reports in the R2c cancer network: analysis of 4521 pathology reports of primary breast tumors].

This study was designed to evaluate the impact on the quality of pathology reports of a cancer network, named R2c covering the west side of PACA region. Over a 7 year-period, we collected 4521 pathology reports on primary breast cancers, filled by pathologists belonging or not the network. The analysis focused on the 6 histo-prognostic factors from the pathology report standardized according to European recommendations. Between the 1997 and 2003 the proportion of reports filled for the 6 factors increased from 29,6 % to 75,1 % among non-member, and from 49,1 % to 89,7 % among members. The histological size or the number of nodes examined is however filled similarly in these two groups. This study shows how the direct implication of the pathologists in a network, with precise criteria improves quality of reports. Nevertheless, network participation is not the only cause of improvement. Having a clinical practice corresponding to the standards determines partly the involvement in a network aiming at rationalizing practices. Moreover, centralised data collection carried out by R2c allows an annual evaluation of the quality of the reports, providing feedback information to members. Lastly, the shared liability in oncology probably has an indirect impact on practices of non-member pathologists.

Adult↗
PubMed2003

Developmental gene network analysis.

The developmental process is controlled by the information processing functions executed by the cis-elements that regulate the expression of the participating genes. A model of the network of cis-regulatory interactions that underlies the specification of the endomesoderm of the sea urchin embryo is analyzed here. Although not all the relevant interactions have yet been uncovered, the model shows how the information processing functions executed by the cis-regulatory elements involved can control essential functions of the specification process, such as transforming the localization of maternal factors into a domain-specific program of gene expression; refining the specification pattern; and stabilizing states of specification. The analysis suggests that the progressivity of the developmental process is also controlled by the cis-regulatory interactions unraveled by the network model. Given that evolution occurs by changing the program for development of the body plan, we illustrate the potential of developmental gene network analysis in understanding the process by which morphological features are maintained and diversify. Comparison of the network of cis-regulatory interactions with a portion of that underlying the specification of the endomesoderm of the starfish illustrates how the similarities and differences provide insights into how the programs for development work and how they evolve.

Animals↗
PubMed1998

Automated quantification of human brain metabolites by artificial neural network analysis from in vivo single-voxel 1H NMR spectra.

A real-time automated way of quantifying metabolites from in vivo NMR spectra using an artificial neural network (ANN) analysis is presented. The spectral training and test sets for ANN containing peaks at the chemical shift ranges resembling long echo time proton NMR spectra from human brain were simulated. The performance of the ANN constructed was compared with an established lineshape fitting (LF) analysis using both simulated and experimental spectral data as inputs. The correspondence between the ANN and LF analyses showed correlation coefficients of order of 0.915-0.997 for spectra with large variations in both signal-to-noise and peak areas. Water suppressed 1H NMR spectra from 24 healthy subjects were collected and choline-containing compounds (Cho), total creatine (Cr), and N-acetyl aspartate (NAA) were quantified with both methods. The ANN quantified these spectra with an accuracy similar to LF analysis (correlation coefficients of 0.915-0.951). These results show that LF and ANN are equally good quantifiers; however, the ANN analyses are more easily automated than LF analyses.

Aspartic Acid↗
PubMed1994

Rapid identification of species within the Mycobacterium tuberculosis complex by artificial neural network analysis of pyrolysis mass spectra.

An artificial neural network (ANN) was trained to distinguish between Mycobacterium tuberculosis and M. bovis with averaged pyrolysis mass spectra from duplicate subcultures of four strains of each of these species, each pyrolysed in triplicate. Once trained, the ANN was interrogated with spectrum data from the original organisms (the "training set") and from 26 other mycobacterial isolates (the "challenge set") of the M. tuberculosis complex (MTBC). Eight strains of M. bovis and 13 of M. tuberculosis, whether sensitive or variously resistant to antituberculosis drugs, were identified in agreement with conventional identification. Four strains of "M. africanum" were identified as M. bovis. Of two atypical M. tuberculosis strains from South India, one was identified as M. tuberculosis and the other as M. bovis. Six strains of BCG proved heterogeneous; two gave equivocal identifications, three were identified as M. bovis and one was identified as M. tuberculosis.

Humans↗
PubMed1993

Rapid identification of streptomycetes by artificial neural network analysis of pyrolysis mass spectra.

An artificial neural network was trained to distinguish between three putatively novel species of Streptomyces using normalised, scaled prolysis mass spectra from three representative strains of each of the taxa, each sampled in triplicate. Once trained, the artificial neural network was challenged with spectral data from the original organisms, the 'training set', from additional members of the putative novel taxa and from over a hundred strains representing six other actinomycete genera. All of the streptomycetes were correctly identified but many of the other actinomycetes were mis-identified. A modified network topology was developed to recognise the mass spectral patterns of the non-streptomycete strains. The resultant neural network correctly identified the streptomycetes, whereas all of the remaining actinomycetes were recognised as unknown organisms. The improved artificial neural network provides a rapid, reliable and cost-effective method of identifying members of the three target streptomycete taxa.

Actinomycetales↗