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PubMedDate not supplied

ECG data compression using Hebbian neural networks.

Principal component analysis has long been used for a variety of signal processing applications, including signal compression. Neural network implementations of principal component analysis provide a means for unsupervised feature discovery and dimension reduction. In this paper, we describe a method for the compression of ECG data using principal component analysis. Hebbian neural networks were used for principal components computation. A variety of examples of normal and pathological ECGs obtained from the MIT ECG database demonstrate that the proposed method can provide compression ratio up to 30 with PRD% less than 5%.

Algorithms↗
PubMed2003

Pathway studio--the analysis and navigation of molecular networks.

SUMMARY: PathwayAssist is a software application developed for navigation and analysis of biological pathways, gene regulation networks and protein interaction maps. It comes with the built-in natural language processing module MedScan and the comprehensive database describing more than 100 000 events of regulation, interaction and modification between proteins, cell processes and small molecules. AVAILABILITY: PathwayAssist is available for commercial licensing from Ariadne Genomics, Inc. The light version with limited functionality will be available for free for academic users at www.ariadnegenomics.com/downloads/.

Database Management Systems↗
PubMed1992

Introduction of a neuronal network as a tool for diagnostic analysis and classification based on experimental pathologic data.

A neuronal network, as well as uni- and multivariate statistics and a discriminant analysis were applied to a morphometric database of 58 cases with thyroid neoplasms and normal thyroid tissue. The ability to classify cases correctly according to their diagnosis was compared between the neuronal network and discriminant analysis. For all pairwise comparisons, classification by neuronal network was as least as good as classification by discriminant analysis. For some comparisons, the neuronal network provided more correct diagnoses than discriminant analysis. On the contrary, in a comparison between tumors which are not significantly different according to multivariate statistics, the network reclassifies only half of the cases correctly, whereas discriminant analysis falsely suggests the possibility of classifying cases with either diagnosis. Our results confirm a higher sensitivity of the neuronal network to the diagnostic information contained in the present morphometric database, and we will therefore use this concept for analysis and diagnostic classification in further morphometric studies.

Diagnosis, Computer-Assisted↗
PubMed1999

Analysis of integrated healthcare networks' performance: a contingency-strategic management perspective.

Few empirical analyses have been done in the organizational researches of integrated healthcare networks (IHNs) or integrated healthcare delivery systems. Using a contingency derived contact-process-performance model, this study attempts to explore the relationships among an IHN's strategic direction, structural design, and performance. A cross-sectional analysis of 100 IHNs suggests that certain contextual factors such as market competition and network age and tax status have statistically significant effects on the implementation of an IHN's service differentiation strategy, which addresses coordination and control in the market. An IHN's service differentiation strategy is positively related to its integrated structural design, which is characterized as integration of administration, patient care, and information system across different settings. However, no evidence supports that the development of integrated structural design may benefit an IHN's performance in terms of clinical efficiency and financial viability.

Cross-Sectional Studies↗
PubMed1985

Mathematical modelling of dynamics and control in metabolic networks. IV. Local stability analysis of single biochemical control loops.

The objective of the study presented herein is to describe the dynamic behavior of a single biochemical control loop, a simple system but an important element of metabolic networks. This loop is a self-regulated sequence of reactions that converts an initial substrate (S) into a final product (P). It consists of three basic elements: (1) a regulated reaction, where the concentration of P controls the flux (I) into the system. This element serves as the control element in the feedback circuit. (2) a sequence of unregulated reactions that leads to the formation of P. This process is to be regulated so that the production rate of P meets a desired target. (3) a process (R) that removes P from the loop to another part of the metabolic network. A mathematical description is formulated that consists of two differential equations and two unspecified functions that represent the reaction rates of I and R. This description is scaled to clarify functional dependence and to attempt a separation of genetic and process determined parameters. The global dynamic behavior of the model is assessed qualitatively by examining the occurrence of static and dynamic bifurcations, multiple steady states or sustained oscillations respectively, via local stability analysis. General criteria for both types of bifurcations are developed without specifying the functional form of I and R, but explicitly accounting for the kinetic properties of the reaction chain. A particularly simple criterion is found for static bifurcations which can appear only for loops with positive feedback, i.e. when the regulated reaction is activated by P. This criterion only contains the properties of I and R. The criteria for dynamic bifurcations, which occur when the feedback interaction is inhibitory, are more complex. These depend strongly on the properties of the reaction chain, and oscillations are favored if the dynamic operator describing the reaction sequence is of high order or if it contains time delays.

Feedback↗
PubMed2004

Reconstructing biological networks using conditional correlation analysis.

MOTIVATION: One of the present challenges in biological research is the organization of the data originating from high-throughput technologies. One way in which this information can be organized is in the form of networks of influences, physical or statistical, between cellular components. We propose an experimental method for probing biological networks, analyzing the resulting data and reconstructing the network architecture. METHODS: We use networks of known topology consisting of nodes (genes), directed edges (gene-gene interactions) and a dynamics for the genes' mRNA concentrations in terms of the gene-gene interactions. We proposed a network reconstruction algorithm based on the conditional correlation of the mRNA equilibrium concentration between two genes given that one of them was knocked down. Using simulated gene expression data on networks of known connectivity, we investigated how the reconstruction error is affected by noise, network topology, size, sparseness and dynamic parameters. RESULTS: Errors arise from correlation between nodes connected through intermediate nodes (false positives) and when the correlation between two directly connected nodes is obscured by noise, non-linearity or multiple inputs to the target node (false negatives). Two critical components of the method are as follows: (1) the choice of an optimal correlation threshold for predicting connections and (2) the reduction of errors arising from indirect connections (for which a novel algorithm is proposed). With these improvements, we can reconstruct networks with the topology of the transcriptional regulatory network in Escherichia coli with a reasonably low error rate.

Algorithms↗
PubMed1996

Quantitative analysis of multivariate data using artificial neural networks: a tutorial review and applications to the deconvolution of pyrolysis mass spectra.

The implementation of artificial neural networks (ANNs) to the analysis of multivariate data is reviewed, with particular reference to the analysis of pyrolysis mass spectra. The need for and benefits of multivariate data analysis are explained followed by a discussion of ANNs and their optimisation. Finally, an example of the use of ANNs for the quantitative deconvolution of the pyrolysis mass spectra of Staphylococcus aureus mixed with Escherichia coli is demonstrated.

Escherichia coli↗
PubMed2003

Application of multivariate analysis and artificial neural networks for the differentiation of red wines from the Canary Islands according to the island of origin.

Eleven metals (K, Na, Ca, Mg, Fe, Cu, Zn, Mn, Sr, Li, and Rb) were determined in 83 red wines from the Canary Islands. The wines presented high concentrations of Na, and the concentrations of Cu and Zn were much lower than the maximum concentrations established by the International Office of Vine and Wine (OIV). Applying principal component analysis, the dimension space was reduced to five principal components that explain 76.4% of the total variance, and the wines tend to separate on the basis of the island of production. Linear discriminant analysis (LDA) allowed a reasonable classification of wines according to the island of production. When artificial neural networks (Kohonen self-organizing maps and back-propagation feed-forward as unsupervised and supervised techniques, respectively) were applied on the matrix of data constituted by the analyzed metals, the results improved in relation to those obtained by other multivariate methods observing a differentiation of wines according to island of production.

Analysis of Variance↗
PubMed2000

Evaluation of prostate cancer patients receiving multiple staging tests, including ProstaScint scintiscans.

BACKGROUND: Multiple serum tests were performed on archival samples from patients who participated in trials to assess the ProstaScint scan staging ability. Traditional statistical analysis as well as artificial neural network (ANN) analysis were employed to evaluate individual patients and the group as a whole. The results were evaluated so that each factor was tested for prognostic value. METHODS: Data obtained from serum tests, bone scans, and ProstaScint scans were evaluated by traditional statistical methods and ANN to determine the individual value in clinical staging of prostate cancer. RESULTS: Two hundred seventy-five patients (180 postprostatectomy, 95 intact prostate) with prostate cancer (14 with distant metastases) were available for analysis. Data available included: clinical state (remission or progression), most recent clinical TNM stage, bone scan, and ProstaScint scan. Serum was tested for prostate-specific membrane antigen(PSMA), prostate-specific antigen(PSA), free PSA (fPSA), and complexed PSA (cPSA). Additional calculations included percent free PSA, and percent complexed PSA. Spearman individual statistical assessment for traditional group evaluation revealed no significant factors for T-stage. The free PSA and complex PSA had a significant association with node (N)-status. The distant metastases (M) stage correlated well with the bone scan and clinical stage. ANN analysis revealed no significant T-stage factors. N-stage factors showed a 95% sensitivity and 49% specificity. These factors included the presence or absence of a prostate, PSA serum levels, bone scan, and ProstaScint scans as major associated indicators. ANN analysis of the important variables for M-stage included ProstaScint scan score, and PSA levels (total, percent complexed, percent free, and fPSA). These factors were associated with a 95% sensitivity and 15% specificity level. CONCLUSIONS: Two hundred seventy-five patients receiving treatment for prostate cancer were evaluated by ANN and traditional statistical analysis for factors related to stage of disease. ANN revealed that PSA levels, determined by a variety of ways, ProstaScint scan, and bone scan, were significant variables that had prognostic value in determining the likelihood of nodal disease, or distant disease in prostate cancer patients.

Bone and Bones↗
PubMed2006

Global analysis of the transcriptional network controlling Xenopus endoderm formation.

A conserved molecular pathway has emerged controlling endoderm formation in Xenopus zebrafish and mice. Key genes in this pathway include Nodal ligands and transcription factors of the Mix-like paired homeodomain class, Gata4-6 zinc-finger factors and Sox17 HMG domain proteins. Although a linear epistatic pathway has been proposed, the precise hierarchical relationships between these factors and their downstream targets are largely unresolved. Here, we have used a combination of microarray analysis and loss-of-function experiments to examine the global regulatory network controlling Xenopus endoderm formation. We identified over 300 transcripts enriched in the gastrula endoderm, including most of the known endoderm regulators and over a hundred uncharacterized genes. Surprisingly only 10% of the endoderm transcriptome is regulated as predicted by the current linear model. We find that Nodal genes, Mixer and Sox17 have both shared and distinct sets of downstream targets, and that a number of unexpected autoregulatory loops exist between Sox17 and Gata4-6, between Sox17 and Bix1/Bix2/Bix4, and between Sox17 and Xnr4. Furthermore, we find that Mixer does not function primarily via Sox17 as previously proposed. These data provides new insight into the complexity of endoderm formation and will serve as valuable resource for establishing a complete endoderm gene regulatory network.

Animals↗
PubMed2003

Prediction of dihydrofolate reductase inhibition and selectivity using computational neural networks and linear discriminant analysis.

A data set of 345 dihydrofolate reductase inhibitors was used to build QSAR models that correlate chemical structure and inhibition potency for three types of dihydrofolate reductase (DHFR): rat liver (rl), Pneumocystis carinii (pc), and Toxoplasma gondii (tg). Quantitative models were built using subsets of molecular structure descriptors being analyzed by computational neural networks. Neural network models were able to accurately predict log IC(50) values for the three types of DHFR to within +/-0.65 log units (data sets ranged approximately 5.5 log units) of the experimentally determined values. Classification models were also constructed using linear discriminant analysis to identify compounds as selective or nonselective inhibitors of bacterial DHFR (pcDHFR and tgDHFR) relative to mammalian DHFR (rlDHFR). A leave-N-out training procedure was used to add robustness to the models and to prove that consistent results could be obtained using different training and prediction set splits. The best linear discriminant analysis (LDA) models were able to correctly predict DHFR selectivity for approximately 70% of the external prediction set compounds. A set of new nitrogen and oxygen-specific descriptors were developed especially for this data set to better encode structural features, which are believed to directly influence DHFR inhibition and selectivity.

Animals↗
PubMed2004

CARRIE web service: automated transcriptional regulatory network inference and interactive analysis.

We present an intuitive and interactive web service for CARRIE (Computational Ascertainment of Regulatory Relationships Inferred from Expression). CARRIE is a computational method that analyzes microarray and promoter sequence data to infer a transcriptional regulatory network from the response to a specific stimulus. This service displays an interactive graph of the inferred network and provides easy access to the evidence for the involvement of each gene in the network. We provide functionality to include network data in KEGG XML (KGML) format in this graph. Our service also provides Gene Ontology annotation to aid the user in forming hypotheses about the role of each gene in the cellular response. The CARRIE web service is freely available at http://zlab.bu.edu/CARRIE-web.

Binding Sites↗
PubMed2002

Using the volumetric indices of telencephalic structures to distinguish Salamandridae and Plethodontidae: comparison of three statistical methods.

The aim of this study was to establish whether appropriate statistical analysis of 16 volumetric indices corresponding to 16 structures making up the entire telencephalon of Urodela could distinguish between two families, Salamandridae and Plethodontidae. We compared the efficiency of three statistical methods (stepwise discriminant analysis, artificial neural networks, correspondence factor analysis) and the information they provide. All three methods found the same species difficult to classify. However, only correspondence factor analysis could suggest explanations for "misclassifications" as it superimposes the two sets of variables, (sub)species and anatomical variables, thus revealing the correlations between them. The bulbus olfactorius accessorius and the caudal mitral cell layer of the bulbus olfactorius principalis were the most discriminatory structures in separating Salamandridae and Plethodontidae. The correspondence factor analysis mapped species very much in line with accepted taxonomy and highlighted several current controversies [e.g. positioning of certain newts (T. marmoratus, T. vulgaris, T. alpestris), of Salamandrina terdigitata, and of members of the genus Euproctus]. Mapping of Plethodontidae was less clear-cut than that of Salamandridae with more overlap among genera but was quite consistent with knowledge of brain structure complexification. We conclude that relationships derived from analyses of telencephalic structures provide valuable information that might help resolve ambiguities; we have coined the term "neurotaxonomy" for this approach.

Animals↗
PubMed2001

Chaos in small-world networks.

A nonlinear small-world network model has been presented to investigate the effect of nonlinear interaction and time delay on the dynamic properties of small-world networks. Both numerical simulations and analytical analysis for networks with time delay and nonlinear interaction show chaotic features in the system response when nonlinear interaction is strong enough or the length scale is large enough. In addition, the small-world system may behave very differently on different scales. Time-delay parameter also has a very strong effect on properties such as the critical length and response time of small-world networks.

Journal Article↗
PubMed2002

Combinatorial synthesis of genetic networks.

A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Here, with the use of a combinatorial method, a library of networks with varying connectivity was generated in Escherichia coli. These networks were composed of genes encoding the transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters. They displayed phenotypic behaviors resembling binary logical circuits, with two chemical "inputs" and a fluorescent protein "output." Within this simple system, diverse computational functions arose through changes in network connectivity. Combinatorial synthesis provides an alternative approach for studying biological networks, as well as an efficient method for producing diverse phenotypes in vivo.

Bacterial Proteins↗
PubMed2003

Approach of the functional evolution of duplicated genes in Saccharomyces cerevisiae using a new classification method based on protein-protein interaction data.

The concept of protein function is widely used and manipulated by biologists. However, the means of the concept and its understanding may vary depending on the level of functionality one considers (molecular, cellular, physiological, etc.). Genomic studies and new high-throughput methods of the post-genomic era provide the opportunity to shed a new light on the concept of protein function: protein-protein interactions can now be considered as pieces of incomplete but still gigantic networks and the analysis of these networks will permit the emergence of a more integrated view of protein function. In this context, we propose a new functional classification method, which, unlike usual methods based on sequence homology, allows the definition of functional classes of protein based on the identity of their interacting partners. An example of such classification will be shown and discussed for a subset of Saccharomyces cerevisiae proteins, accounting for 7% of the yeast proteome. The genome of the budding yeast contains 50% of protein-coding genes that are paralogs, including 457 pairs of duplicated genes coming probably from an ancient whole genome duplication. We will comment on the functional classification of the duplicated genes when using our method and discuss the contribution of these results to the understanding of function evolution for the duplicated genes.

Evolution, Molecular↗
PubMed2003

Hydroxypropyl cellulose as an adsorptive coating sieving matrix for DNA separations: artificial neural network optimization for microchip analysis.

Effective DNA separations in microelectrophoretic systems are complicated by the need to passivate the surface dynamically or covalently. We describe the optimization and utilization of a novel buffer system for fast DNA separations by capillary and microchip electrophoresis without the need for any surface modification or conditioning prior to separation. At concentrations as high as 5%, hydroxypropyl cellulose (HPC) has a relatively low viscosity, allowing for microchip channel filling to be performed with ease. A MES/TRIS buffer system at pH 6.1 eliminates the need for surface preconditioning procedures due to the promotion of hydrogen bonding of HPC with the wall. An additional benefit with this buffer system is the low current observed at high fields when compared to other common DNA separation buffers. An artificial neural network (ANN) was used to model the data and to predict the optimum conditions. Utility of the ANN-optimized system for molecular diagnostic testing was demonstrated by performing microchip separations on DNA samples from patients suspected of having genetic mutations associated with Duchenne muscular dystrophy (DMD). Microchip analysis easily allowed for the patient samples positive for DMD mutations to be distinguished from patient samples negative for the disease.

Adsorption↗
PubMed2002

[Community activities in primary care in Spain. An analysis based on the network of the Program of Community Activities (PACAP)].

OBJECTIVE: To describe, analyse and discuss the activities in the Network of Community Activities of the Programme of Community Activities in Primary Care of the Spanish Society of Family and Community Medicine. DESIGN: Description of the activities within this Network.Setting. Network of Community Activities of the Spanish Society of Family and Community Medicine. MAIN MEASUREMENTS: Specifications of the variables of geography, target population and experience descriptives were obtained from the qualitative analysis of the activity summary composed by its authors. The measurements are frequency tables expressed in graphs and analysis of the summaries contributed by the groups on objectives, kinds of programme, methodologies, evaluation and conclusions reached. The community orientation activities undertaken by the health centres registered on the Network came mostly (54%) from the autonomous communities of Madrid and Andalusia. A great many of them were aimed at the adult population, tackling problems of chronic diseases, and particularly at women, in this case tackling gender themes such as menopause and pregnancy, etc. CONCLUSIONS: There was uneven distribution between autonomous communities of the experiences included on the web. Central to community orientation are the replies to questions such as: inside or out of the health centre?, the importance of transferring leadership to society, and adaptation to the needs and demands of the population cared for.

Adult↗