Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Networks”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

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

At least 91 records · Page 5Linked to original sources
PubMed1999

NetWork: an interactive interface to the tools for analysis of genetic network structure and dynamics.

We designed a Java applet called NetWork which enables a user to interactively construct and visualize a genetic network of interest, and to and to evaluate and explore its dynamics in the framework of a Boolean network model. NetWork displays the mechanism of gene interactions at the level of gene expression and enables the visualization of large genetic networks. NetWork can serve as an interactive interface to tools for the analysis of genetic network structure and behavior.

Algorithms↗
PubMed2005

Utilizing evolutionary information and gene expression data for estimating gene networks with bayesian network models.

Since microarray gene expression data do not contain sufficient information for estimating accurate gene networks, other biological information has been considered to improve the estimated networks. Recent studies have revealed that highly conserved proteins that exhibit similar expression patterns in different organisms, have almost the same function in each organism. Such conserved proteins are also known to play similar roles in terms of the regulation of genes. Therefore, this evolutionary information can be used to refine regulatory relationships among genes, which are estimated from gene expression data. We propose a statistical method for estimating gene networks from gene expression data by utilizing evolutionarily conserved relationships between genes. Our method simultaneously estimates two gene networks of two distinct organisms, with a Bayesian network model utilizing the evolutionary information so that gene expression data of one organism helps to estimate the gene network of the other. We show the effectiveness of the method through the analysis on Saccharomyces cerevisiae and Homo sapiens cell cycle gene expression data. Our method was successful in estimating gene networks that capture many known relationships as well as several unknown relationships which are likely to be novel. Supplementary information is available at http://bonsai.ims.u-tokyo.ac.jp/~tamada/bayesnet/.

Bayes Theorem↗
PubMed2005

Dynamic properties of network motifs contribute to biological network organization.

Biological networks, such as those describing gene regulation, signal transduction, and neural synapses, are representations of large-scale dynamic systems. Discovery of organizing principles of biological networks can be enhanced by embracing the notion that there is a deep interplay between network structure and system dynamics. Recently, many structural characteristics of these non-random networks have been identified, but dynamical implications of the features have not been explored comprehensively. We demonstrate by exhaustive computational analysis that a dynamical property--stability or robustness to small perturbations--is highly correlated with the relative abundance of small subnetworks (network motifs) in several previously determined biological networks. We propose that robust dynamical stability is an influential property that can determine the non-random structure of biological networks.

Animals↗
PubMed1986

Medicare program; End Stage Renal Disease Program; redesignation of networks and reorganization of network organizations--HCFA. Final rule.

This final rule revises the requirements in current regulations pertaining to the End-Stage Renal Disease (ESRD) networks and organizations and establishes provisions for new, more efficient network organizations. This rule removes the criteria that define existing networks, removes the requirement that HCFA change designations of ESRD networks through rulemaking, and removes the list of currently-designated networks that now appears in regulations. It is intended that these amendments will increase the efficiency and effectiveness of the ESRD program by instituting a faster process for changing network designations and organizations as program needs arise. These amendments also permit the reduction of the number of existing networks to as few as 14, which is consistent with section 9214 of Pub. L. 99-272, the Consolidated Omnibus Budget Reconciliation Act of 1985 (COBRA).

Centers for Medicare and Medicaid Services, U.S.↗
PubMed2003

Influence of network topology and data collection on network inference.

We recently developed an approach for testing the accuracy of network inference algorithms by applying them to biologically realistic simulations with known network topology. Here, we seek to determine the degree to which the network topology and data sampling regime influence the ability of our Bayesian network inference algorithm, NETWORKINFERENCE, to recover gene regulatory networks. NETWORKINFERENCE performed well at recovering feedback loops and multiple targets of a regulator with small amounts of data, but required more data to recover multiple regulators of a gene. When collecting the same number of data samples at different intervals from the system, the best recovery was produced by sampling intervals long enough such that sampling covered propagation of regulation through the network but not so long such that intervals missed internal dynamics. These results further elucidate the possibilities and limitations of network inference based on biological data.

Algorithms↗
PubMed2002

Multi-objective cooperative coevolution of artificial neural networks (multi-objective cooperative networks).

In this paper we present a cooperative coevolutive model for the evolution of neural network topology and weights, called MOBNET. MOBNET evolves subcomponents that must be combined in order to form a network, instead of whole networks. The problem of assigning credit to the subcomponents is approached as a multi-objective optimization task. The subcomponents in a cooperative coevolutive model must fulfill different criteria to be useful, these criteria usually conflict with each other. The problem of evaluating the fitness on an individual based on many criteria that must be optimized together can be approached as a multi-criteria optimization problems, so the methods from multi-objective optimization offer the most natural way to solve the problem. In this work we show how using several objectives for every subcomponent and evaluating its fitness as a multi-objective optimization problem, the performance of the model is highly competitive. MOBNET is compared with several standard methods of classification and with other neural network models in solving four real-world problems, and it shows the best overall performance of all classification methods applied. It also produces smaller networks when compared to other models. The basic idea underlying MOBNET is extensible to a more general model of coevolutionary computation, as none of its features are exclusive of neural networks design. There are many applications of cooperative coevolution that could benefit from the multi-objective optimization approach proposed in this paper.

Biological Evolution↗
PubMed1992

An overview of networking for physicians and problems of network consulting in remote areas.

For computer networking the most suitable operating systems are UNIX or MS-DOS. As networking software UUCP and TCP/IP are most common. Hardware requirements are derived from the operating system and from the networking software. Low-cost solutions, for example, uuPC, a public domain version of UUCP, require only an 8088 processor and a 2400-baud modem. TCP/IP fares better with more powerful processors and requires permanent lines between the connecting computers. In developing countries the introduction of computer networks is hampered by several factors: lack of foreign exchange, price of hardware and software, unreliable electricity and telephone lines, lack of hardware and software support, large distances to the nearest center, and incompatibilities between existing systems and the network. Important aspects for clinical networking in developing countries include appointment scheduling in the referral hospitals, access to laboratory and pathology results from the central laboratory, and primary health care information such as epidemiologic data. Advanced systems, for example, for image processing, are not yet feasible in developing countries.

Computer Communication Networks↗
PubMed1996

Using a feed-forward network to incorporate the relation between attractees and attractors in a generalized discrete Hopfield network.

This paper demonstrates how a feedforward network with constant connection matrices may be used to train a Hopfield style network for pattern recognition. The connection matrix of the Hopfield style network is asymmetric and its diagonal is non-zero. The Hopfield style network referred to as a GDHN is trained to incorporate a relation between attractees and attractors. The attractees represent class samples and the attractors represent class prototypes. The feedforward network is trained using a gradient descent method. Gradients are fed forward in the network to obtain a gradient for a cost function.

Algorithms↗
PubMed2001

Networks in later life: an examination of race differences in social support networks.

Although there has been considerable interest in the effects of social support networks on various health outcomes for older adults, there has been little research directed toward the predictors of networks. In this study, we examine race differences in the determinants of social support network characteristics (size, frequency of interaction with network members, proportion of kin, and amount of support received and given to network members) using data from an older community sample drawn from the North Carolina site of the Established Populations for Epidemiologic Studies of the Elderly (EPESE) focusing on adults sixty-five and older (n = 4124). This research focuses on the extent to which race differences in network dimensions are present and whether these variations can be attributed to varying social structural positions held by African Americans and Whites. The results indicate that several race differences persist even when controlling for social structural variables. The structural argument and future implications are discussed.

Black or African American↗
PubMed2004

Merging race models and adaptive networks: a parallel race network.

This article presents a generalization of race models involving multiple channels. The major contribution of this article is the implementation of a learning rule that enables networks based on such a parallel race model to learn stimulus-response associations. This model is called a parallel race network. Surprisingly, with a two-layer architecture, a parallel race network learns the XOR problem without the benefit of hidden units. The model described here can be seen as a reduction-of-information system (Haider & Frensch, 1996). An emergent property of this model is seriality: In some conditions, responses are performed with a fixed order, although the system is parallel. The mere existence of this supervised network demonstrates that networks can perform cognitive processes without the weighted sum metric that characterizes strength-based networks.

Association↗
PubMed1984

A neural network model for the mechanism of feature-extraction. A self-organizing network with feedback inhibition.

We propose a new multilayered neural network model which has the ability of rapid self-organization. This model is a modified version of the cognitron (Fukushima, 1975). It has modifiable inhibitory feedback connections, as well as conventional modifiable excitatory feedforward connections, between the cells of adjoining layers. If a feature-extracting cell in the network is excited by a stimulus which is already familiar to the network, the cell immediately feeds back inhibitory signals to its presynaptic cells in the preceding layer, which suppresses their response. On the other hand, the feature-extracting cell does not respond to an unfamiliar feature, and the responses from its presynaptic cells are therefore not suppressed because they do not receive any feedback inhibition. Modifiable synapses in the new network are reinforced in a way similar to those in the cognitron, and synaptic connections from cells yielding a large sustained output are reinforced. Since familiar stimulus features do not elicit a sustained response from the cells of the network, only circuits which detect novel stimulus features develop. The network therefore quickly acquires favorable pattern-selectivity by the mere repetitive presentation of a set of learning patterns.

Animals↗
PubMed1980

Task force report: social networks as mediators of social support: an analysis of the effects and determinants of social networks.

The intent of this paper is to present a representative, though not exhaustive, overview of the current literature on social networks, with an emphasis on research linking social networks to psychological adaptation. This overview includes a review of social network concepts; and analysis of the multiple determinants of social networks; an analysis of the varied effects of social networks; and the implications for policies and practices of community mental health centers. This paper adopts the view that the concept of social network is a useful tool in examining both the functional and the dysfunctional influences of one's primary group on individual adaption.

Adaptation, Psychological↗
PubMed1990

Molecular networks as a sub-neural factor of neural networks.

We describe a new approach in the research of neural networks. This research is based on molecular networks in the neuron. If we use molecular networks as a sub-neuron factor of neural networks, it is a more realistic approach than today's concepts in this new computer technology field, because the artificial neural activity profile is similar to the profile of the action potential in the natural neuron. The molecular networks approach can be used in three technologies: neurocomputer, neurochip and molecular chip. This means that molecular networks open new fields of science and engineering called molecular-like machines and molecular machines.

Cytoskeleton↗
PubMed2005

Systems analysis of a quorum sensing network: design constraints imposed by the functional requirements, network topology and kinetic constants.

Understanding the relationship between the structural organization of intracellular decision networks and the observable phenotypes they control is one of the exigent problems of modern systems biology. Here we perform a systems analysis of a prototypic quorum sensing network whose operation allows bacterial populations to activate certain patterns of gene expression cooperatively. We apply structural perturbations to the model and analyze the resulting changes in the network behavior with the aim to identify the contribution of individual network elements to the functional fitness of the whole network. Specifically, we demonstrate the importance of the dimerization of the transcription factor and the presence of the auxiliary positive feedback loop on the switch-like behavior of the network and the stability of its "on" and "off" states under the influence of molecular noise.

Bacterial Proteins↗
PubMed2006

Control of Boolean networks: hardness results and algorithms for tree structured networks.

Finding control strategies of cells is a challenging and important problem in the post-genomic era. This paper considers theoretical aspects of the control problem using the Boolean network (BN), which is a simplified model of genetic networks. It is shown that finding a control strategy leading to the desired global state is computationally intractable (NP-hard) in general. Furthermore, this hardness result is extended for BNs with considerably restricted network structures. These results justify existing exponential time algorithms for finding control strategies for probabilistic Boolean networks (PBNs). On the other hand, this paper shows that the control problem can be solved in polynomial time if the network has a tree structure. Then, this algorithm is extended for the case where the network has a few loops and the number of time steps is small. Though this paper focuses on theoretical aspects, biological implications of the theoretical results are also discussed.

Algorithms↗
PubMed2004

Hierarchical thinking in network biology: the unbiased modularization of biochemical networks.

As reconstructed biochemical reaction networks continue to grow in size and scope, there is a growing need to describe the functional modules within them. Such modules facilitate the study of biological processes by deconstructing complex biological networks into conceptually simple entities. The definition of network modules is often based on intuitive reasoning. As an alternative, methods are being developed for defining biochemical network modules in an unbiased fashion. These unbiased network modules are mathematically derived from the structure of the whole network under consideration.

Animals↗