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At least 181 records · Page 10Linked to original sources
PubMed2003

Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network.

We propose a new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is the estimation of the conditional distribution of each random variable. We consider fitting nonparametric regression models with heterogeneous error variances to the microarray gene expression data to capture the nonlinear structures between genes. Selecting the optimal graph, which gives the best representation of the system among genes, is still a problem to be solved. We theoretically derive a new graph selection criterion from Bayes approach in general situations. The proposed method includes previous methods based on Bayesian networks. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae gene expression data newly obtained by disrupting 100 genes.

Bayes Theorem↗
PubMed2004

Combining microarrays and biological knowledge for estimating gene networks via bayesian networks.

We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-protein interactions, protein-DNA interactions, binding site information, existing literature and so on. Microarray data do not contain enough information for constructing gene networks accurately in many cases. Our method adds biological knowledge to the estimation method of gene networks under a Bayesian statistical framework, and also controls the trade-off between microarray information and biological knowledge automatically. We conduct Monte Carlo simulations to show the effectiveness of the proposed method. We analyze Saccharomyces cerevisiae gene expression data as an application.

Algorithms↗
PubMedDate not supplied

[The Environment and Childhood Research Network ("INMA" network): study protocol].

Increasingly greater evidence exists as to the influence which diet and exposure to low doses of toxic substances during the prenatal stage and early childhood has on health and well-being throughout later stages of life. Following the WHO and European Union recommendations in 2003, the Cooperative Environment and Childhood Research Network was set up to study the effects of the environment and diet on fetal and early childhood development in different geographical areas of Spain. This Network integrates different multidisciplinary research groups and is comprised of six cohorts--three pre-existing and three de novo--which will follow up prospectively 3,600 pregnant women, from the start of pregnancy up to age 4-6 years of the child. This network's general objectives are: (1) To describe individual exposure to toxic substances in the environment during gestation and early childhood. (2) To evaluate the effects of exposure to toxic substances and diet on fetal and early childhood development. (3) To evaluate the interaction among toxic, nutritional and genetic factors in fetal and early childhood development. The follow-up is done every three months during gestation, at birth, at age one and up to age four or six. The information is gathered by means of questionnaires, clinical data, physical examinations, echographs, biomarkers and environmental measurements. The general characteristics of the network and a description of the current situation of each one of the cohorts are provided in this study.

Child↗
PubMedDate not supplied

3-D components of a biological neural network visualized in computer generated imagery. II. Macular neural network organization.

Computer-assisted reconstructions of small parts of the macular neural network show how the nerve terminals and receptive fields are organized in 3-dimensional space. This biological neural network is anatomically organized for parallel distributed processing of information. Processing appears to be more complex than in computer-based neural networks, because spatiotemporal factors figure into synaptic weighting. Serial reconstruction data show anatomical arrangements which suggest that 1) assemblies of cells analyse and distribute information with inbuilt redundancy, to improve reliability; 2) feedforward/feedback loops provide the capacity for presynaptic modulation of output during processing; 3) constrained randomness in connectivities contributes to adaptability; and 4) local variations in network complexity permit differing analyses of incoming signals to take place simultaneously. The last inference suggests that there may be segregation of information flow to central stations subserving particular functions.

Acoustic Maculae↗
PubMed1991

Network thermodynamic analysis of vasomotion in a microvascular network.

The modulation of microvascular blood flow by vasomotion in the individual vessels of a simple vascular network was simulated by means of a network thermodynamic model. The flow is driven under a pulsating pressure through two arcades of branching vasoactive arterioles into a passive resistance representing the capillary and venular beds. Each vessel was assumed to have the capability of decreasing rhythmically the local diameter over a short section by a specified fraction of the maximum value and to change the average diameter along its total length in response to alterations in intraluminal pressure. Blood was assumed to exhibit a simple linear viscous flow resistance. Alterations in flow rate and distribution through the network were determined as a function of the magnitude and frequency of vasomotion within the individual arterioles supplying blood to the microvascular bed. Specific cases are shown to illustrate how blood flow can be influenced by the patterns of vasomotion within the network.

Arterioles↗
PubMed2000

Influenza immunization rates in the Intermountain End-Stage Renal Disease Network (Network 15).

Billing data from the Health Care Financing Administration (HCFA) indicated that the influenza immunization rates for dialysis patients in the United States do not meet the goal of 60% set by Healthy People 2000, and fall significantly short of the goal of 90% of all Medicare beneficiaries as outlined in Healthy People 2010. Influenza and pneumonia together are the sixth leading cause of death in the United States. Despite the known benefits of influenza vaccination in reducing morbidity and mortality, only 40% to 50% of high-risk patients are immunized. Although HCFA/Medicare billing data may not provide the best measurement of actual practice, it is currently the only measure available from any national source. The data suggest that there is a need for improvement. Because the HCFA/Medicare rates were based only on those immunizations for which Medicare was billed, End-Stage Renal Disease Network 15 embarked on a project to determine a more accurate rate of immunization within the Network based on information provided by the dialysis facilities. Influenza vaccination rates for the winter 1998 flu season ranged from 51.5% to 84.9% for the states in the Network; the rate for the whole Network was 74.6%. The HCFA/Medicare billed influenza immunization rates were 26.5 to 45.6 percentage points lower.

Arizona↗
PubMed1998

Finding Genetic Network from Experiments by Weighted Network Model.

We study the problem of finding a genetic network from data obtained by multiple gene disruptions and overexpressions. We define a genetic network as a weighted graph, and analyze the computational complexity of the problem. We show that if there exists a weighted network which is consistent with given data, we can find it in polynomial time. Moreover, we also consider the optimization problem, where we try to find an optimally consistent weighted network with given data. We show that the problem is NP-hard. On the other hand, we give a polynomial-time approximation algorithm to solve it with approximation ratio 2. We report some simulation results on experiments.

Journal Article↗
PubMed2005

[Analysis of the medical activity related to cancer in a network of multidisciplinary hospitals using claims databases, the reseau Concorde Oncology Network].

Recently, to answer patients, caregivers and professionals needs, the "Plan Cancer" has been presented by the French Government. This plan is intended to improve quality of care in cancer patients and finally, patients' survival and quality of life. This planned strategy stresses the importance of organized interactions between hospitals and between the various health professionals. Measuring the number of patients with cancer and the activity related to cancer in large networks of multidisciplinary hospitals has became a real challenge in France for organizational, quality of care and economic reasons. Many University Hospitals in France have chosen to face this question by using the French DRG based information system called PMSI. It allows estimating the proportion of hospital stays concerned by cancers that are identified with algorithms based on ICD 10. However, French databases of hospital discharges do not allow patients identification. We collected data on hospital stays and patients in a subset of an organized network focused on cancer care and composed of 55 public or private hospitals in the Rhone-Alpes area. We used these data to estimate the number of patients who had been hospitalized within the network in 2000. Approximately 110,000 hospital stays were related with a diagnostic of cancer, corresponding to a number of patients within a range of 30345 to 35700. In absence of communicating files between hospitals, claims databases are an interesting source of information for cancer burden. The recent implementation of a procedure allowing the linkage of data concerning each patient should permit better estimates in the future. The main limitation will remain the possibility of a hospital to participate to more than one network.

Adolescent↗
PubMed2002

Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network.

We propose a new statistical method for constructing genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is in the estimation of the conditional distribution of each random variable. We consider fitting nonparametric regression models with heterogeneous error variances to the microarray gene expression data to capture the nonlinear structures between genes. A problem still remains to be solved in selecting an optimal graph, which gives the best representation of the system among genes. We theoretically derive a new graph selection criterion from Bayes approach in general situations. The proposed method includes previous methods based on Bayesian networks. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae gene expression data newly obtained by disrupting 100 genes.

Artificial Intelligence↗
PubMed2005

Issue network versus producer network? ASH, the Tobacco Products Research Trust and UK smoking policy.

Policy science studies of networks in smoking policy segment the smoking arena into a "producer network" of industrial and retail interests and an "issue network" of anti-smoking organisations. Case studies of ASH (Action on Smoking and Health) and the Tobacco Products Research Trust (TPRT) indicate that networks in smoking policy were more complex and overlapping. ASH pioneered a new style of media-conscious health activism in the 1970s with an anti-industry line. Nevertheless the strategy of harm reduction remained an objective for industry and government, and also for some public health interests through the work of the TPRT. First safer smoking and then nicotine were the focus of these activities.

Health Policy↗
PubMed2003

Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks.

We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-protein interactions, protein-DNA interactions, binding site information, existing literature and so on. Unfortunately, microarray data do not contain enough information for constructing gene networks accurately in many cases. Our method adds biological knowledge to the estimation method of gene networks under a Bayesian statistical framework, and also controls the trade-off between microarray information and biological knowledge automatically. We conduct Monte Carlo simulations to show the effectiveness of the proposed method. We analyze Saccharomyces cerevisiae gene expression data as an application.

Bayes Theorem↗
PubMedDate not supplied

[The organization of the network characteristics of the cells in local and distributed neuronal networks of the cat brain].

In cats with elaborated alimentary instrumental reflexes to light net characteristics of neurones of visual, motor cortex and the hypothalamus lateral nucleus were studied on the basis of revealed interneuronal interactions by means of cross-correlation method of analysis. Different organization of net properties of the cortical neurones in organization of local and distributed neuronal networks was shown, namely: predominance of the divergent characteristics over the convergent ones for cells in local networks and levelling of these relations in distributed nets. Neurones of the lateral hypothalamus nucleus had equal presentation of divergent and convergent properties in organization of local and distributed networks. Net characteristics of neurones of the cortical and subcortical structures were manifested in the background after the elaboration and the extinction of conditioned reflexes. Only small cells of the visual cortex were functionally dependent and changed correlation of net characteristics in local networks at CR extinction.

Animals↗
PubMed1984

[Primary network and mental health : results of interventions with identified patients and their primary network.].

The results of a study attempting the systematic application of a network intervention approach to meet the therapeutic needs of 20 non-preselected clients at an outpatient psychiatric clinic showed that this community oriented approach was not only applicable but also beneficial. While our principal objective was to see if a network approach could be adapted to the treatment of an ongoing public psychiatric service for a given sector of the population, our results indicate certains additional trends : those clients who accepted to participate in a network approach (2/3) not only showed decreased symptomatology but also increased personal satisfaction and improvements in their social life. Meanwhile those clients who underwent individual, couple, or family therapy but who did not accept a network approach showed decreases in symptomatology only, without corresponding improvement in other spheres.

English Abstract↗
PubMed1983

Lessons from the network and next steps: the Wisconsin Head and Neck Cancer Network.

In summary, the Wisconsin Head and Neck Cancer Control Network was formulated with the primary charge of improving the management of patients with head and neck cancer throughout the State. Detailed data on over 1500 patients representing 80 percent of all head and neck cancer patients in the State of Wisconsin was gathered, reviewed for quality, and analyzed. The data base consisted of information on the symptoms, signs, physical findings, treatment, complications, morbidity, and outcome. Although it was the impression of Network members that lesions were being noted earlier and treatment perhaps more effective, it was not possible to document this impression with the available data. The benchmarks of success of the overall project were numerous, however, particularly in the areas of public and professional education. The individuals reached were extremely receptive to new information. Several patients indicated that their dentist and dental hygienists, for example, were performing more thorough oral examinations. The effort was successful in establishing a network of physicians and institutions throughout the State cooperating with a common interest. It is hoped that in the future we can continue to build on the experience of the Network. Follow-up data has continued to accumulate through a largely voluntary effort of the coinvestigators, and this will enhance the value of the data base. Such follow-up data will certainly facilitate ongoing and future research projects based on this comprehensive information. To date, one publication has been prepared since termination of the project using the data base.

Data Collection↗
PubMed1994

Potential of practice-based research networks: experiences from ASPN. Ambulatory Sentinel Practice Network.

During the past 20 years, the feasibility of practice-based research in networks has been established in the United States. The initial work of these networks has revealed the need for a better understanding of family practice and the rest of primary care in order to address the challenges facing our health care system. This paper explores the nature, potential, and limitations of practice-based research networks based on the results of a dozen studies conducted by the Ambulatory Sentinel Practice Network (ASPN).

Adolescent↗
PubMed1993

Lattice neural network minimization. Application of neural network optimization for locating the global-minimum conformations of proteins.

A way of formulating the protein-folding problem in neural network optimization terms is presented in this paper. This is accomplished by representing the conformation of a protein as an array of the amino acid sequence versus position on a three-dimensional face-centered cubic lattice with an energy function defined in terms of the array variables. The method is called lattice neural network minimization (LNNM). Using the neural network minimization method, the energy function is minimized to locate the global minimum energy for the conformation of the protein. The energy function consisted of site exclusion and bond connectivity penalty terms and a pairwise contact energy potential. The contact energy potential used in the procedure is the united-residue potential of Miyazawa, Jernigan and Covell. The LNNM method found the global minimum for a seven-residue peptide in all of the 15 runs carried out. The time for each run was approximately 30 seconds on one processor of an IBM 3090 computer. For a nine-residue peptide, the global minimum was found in 7 out of 15 runs (47%) in approximately 50 seconds per run. For this peptide, LNNM found the global minimum or the second lowest minimum in 10 of the runs. In the same total CPU times (approximately 750 seconds), a Monte Carlo simulated annealing method found the global minimum or the second lowest minimum in only two runs, demonstrating the superiority of LNNM over the standard Monte Carlo simulated annealing method for this nine-residue peptide. Starting from a uniform array for the protein crambin (46 residues) on the lattice, the energy of the crambin array was minimized and a compact low-energy structure was found in approximately 25 minutes of CPU time. Its energy was much lower than that of the native protein, suggesting that there are inadequacies in the Miyazawa-Jernigan-Covell potential. The LNNM method was applied to the prediction of what was previously called nucleation but more properly called chain-folding initiation sites (CFIS) of a protein. LNNM correctly predicted the CFIS for the two proteins examined, RNase S and T4 lysozyme. The LNNM method was also applied to another chain optimization problem, minimization of the root-mean-square distance error (r.m.s.d.) (a measure similar to r.m.s. deviation) in fitting X-ray structures to a lattice, with good results.

Models, Chemical↗
PubMed2001

Learning to network and networking to learn: facilitating the process of adaptive management in a local response to the UK's National Air Quality Strategy.

The adaptive management leitmotiv of "learning to manage and managing to learn" sets out an attractive agenda for dealing with the overwhelming complexity of environmental phenomena that humans have problematized. To ensure that this rallying cry translates into effective action, it is important to give consideration to structures and procedures for facilitating the efforts of those willing or able to respond to the adaptive management call. To date, calls to establish the right organization to coordinate multiagency responses have tended to emphasize the noun, or bounded-entity, sense of the word organization. We believe that this is at the expense of its other, verb or process, connotation. In this paper, rather than searching for the perfect organization structure that mandates mutual trust and collective action shaped by all relevant parties' perspectives and possible contributions, we direct attention towards the process of nurturing integrated adaptive responses among individuals who have diverse organizational allegiances. By shifting the balance towards the process connotation of the right organization, we hope that a new mindscape can be discerned for those interested in putting adaptive management principles into practice. We seek to conjure up an image of this mindscape through the phrase "learning to network and networking to learn," and set out to strengthen this by demonstrating how adaptive response networks can arise from the mutually defining relationship between stakeholders and issues. This is demonstrated through a local response to the United Kingdom's National Air Quality Strategy.

Air Pollution↗
PubMed2005

On the relationship between deterministic and probabilistic directed Graphical models: from Bayesian networks to recursive neural networks.

Machine learning methods that can handle variable-size structured data such as sequences and graphs include Bayesian networks (BNs) and Recursive Neural Networks (RNNs). In both classes of models, the data is modeled using a set of observed and hidden variables associated with the nodes of a directed acyclic graph. In BNs, the conditional relationships between parent and child variables are probabilistic, whereas in RNNs they are deterministic and parameterized by neural networks. Here, we study the formal relationship between both classes of models and show that when the source nodes variables are observed, RNNs can be viewed as limits, both in distribution and probability, of BNs with local conditional distributions that have vanishing covariance matrices and converge to delta functions. Conditions for uniform convergence are also given together with an analysis of the behavior and exactness of Belief Propagation (BP) in 'deterministic' BNs. Implications for the design of mixed architectures and the corresponding inference algorithms are briefly discussed.

Bayes Theorem↗