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PubMed2002

Towards a bioinformatics network for Latin America and the Caribbean (LACBioNet).

Bioinformatics is increasingly recognised as a crucial field for research and development in the biological sciences, and forms an integral part of genomics, proteomics and modern biotechnology. Worldwide participation is important, and scientists in developing countries can contribute to this field. Regional networks for bioinformatics are highly beneficial for capacity strengthening and cooperation, and for establishing productive interactions between scientists in the fields of biological and informatics sciences. Such a network (LACBioNet) is being organised for Latin America and the Caribbean. Its immediate goals include the organisation and extension of nodes and services, information and communication, research and development in different specialty fields of bioinformatics, and training and human resource development.

Caribbean Region↗
PubMed2005

Networks and epidemic models.

Networks and the epidemiology of directly transmitted infectious diseases are fundamentally linked. The foundations of epidemiology and early epidemiological models were based on population wide random-mixing, but in practice each individual has a finite set of contacts to whom they can pass infection; the ensemble of all such contacts forms a 'mixing network'. Knowledge of the structure of the network allows models to compute the epidemic dynamics at the population scale from the individual-level behaviour of infections. Therefore, characteristics of mixing networks-and how these deviate from the random-mixing norm-have become important applied concerns that may enhance the understanding and prediction of epidemic patterns and intervention measures. Here, we review the basis of epidemiological theory (based on random-mixing models) and network theory (based on work from the social sciences and graph theory). We then describe a variety of methods that allow the mixing network, or an approximation to the network, to be ascertained. It is often the case that time and resources limit our ability to accurately find all connections within a network, and hence a generic understanding of the relationship between network structure and disease dynamics is needed. Therefore, we review some of the variety of idealized network types and approximation techniques that have been utilized to elucidate this link. Finally, we look to the future to suggest how the two fields of network theory and epidemiological modelling can deliver an improved understanding of disease dynamics and better public health through effective disease control.

Animals↗
PubMed1984

Social support and social network scales: a psychometric review.

Social support and social network scales were reviewed from a psychometric perspective. A sample of 29 behavioral science studies were selected from social support and social network articles published between 1967 and 1982. Each study was classified according to psychometric issues of scaling, reliability, and validity. The review was motivated by the hypothesis that many social support and social network scales are developed without complete utilization of existing measurement technology. Results of this review confirm this hypothesis. Limited availability of basic psychometric data for such scales attenuates theory development and research efficiency. Researchers in this field are encouraged to increase the availability of basic psychometric information for social support and social network scales.

Humans↗
PubMed2003

Actor-network theory: a tool to support ethical analysis of commercial genetic testing.

Social, ethical and policy analysis of the issues arising from gene patenting and commercial genetic testing is enhanced by the application of science and technology studies, and Actor-Network Theory (ANT) in particular. We suggest the potential for transferring ANT's flexible nature to an applied heuristic methodology for gathering empirical information and for analysing the complex networks involved in the development of genetic technologies. Three concepts are explored in this paper--actor-networks, translation, and drift--and applied to the case of Myriad Genetics and their commercial BRACAnalysis genetic susceptibility test for hereditary breast cancer. Treating this test as an active participant in socio-technical networks clarifies the extent to which it interacts with, shapes and is shaped by people, other technologies, and institutions. Such an understanding enables more sophisticated and nuanced technology assessment, academic analysis, as well as public debate about the social, ethical and policy implications of the commercialization of new genetic technologies.

Breast Neoplasms↗
PubMed1986

End-user information utilities in the health sciences.

Electronic information utilities in the health sciences such as MINET (Medical Information Network) are increasingly important as it becomes more convenient for end users to retrieve data and use various online information services such as electronic mail from personal computer workstations. MINET is examined in depth as the most fully developed end-user utility. Several other end-user utilities and full-text databases in the health sciences are listed and described.

MEDLARS↗
PubMed2005

Generating uniformly distributed random networks.

The analysis of real networks taken from the biological, social, and physical sciences often requires a carefully posed statistical null-hypothesis approach. One common method requires comparing real networks to an ensemble of random matrices that satisfy realistic constraints in which each different matrix member is equiprobable. We discuss existing methods for generating uniformly distributed (constrained) random matrices, describe their shortcomings, and present an efficient technique that should have many practical applications.

Journal Article↗
PubMed2004

"Harnessing genomics to improve health in India" - an executive course to support genomics policy.

BACKGROUND: The benefits of scientific medicine have eluded millions in developing countries and the genomics revolution threatens to increase health inequities between North and South. India, as a developing yet also industrialized country, is uniquely positioned to pioneer science policy innovations to narrow the genomics divide. Recognizing this, the Indian Council of Medical Research and the University of Toronto Joint Centre for Bioethics conducted a Genomics Policy Executive Course in January 2003 in Kerala, India. The course provided a forum for stakeholders to discuss the relevance of genomics for health in India. This article presents the course findings and recommendations formulated by the participants for genomics policy in India. METHODS: The course goals were to familiarize participants with the implications of genomics for health in India; analyze and debate policy and ethical issues; and develop a multi-sectoral opinion leaders' network to share perspectives. To achieve these goals, the course brought together representatives of academic research centres, biotechnology companies, regulatory bodies, media, voluntary, and legal organizations to engage in discussion. Topics included scientific advances in genomics, followed by innovations in business models, public sector perspectives, ethics, legal issues and national innovation systems. RESULTS: Seven main recommendations emerged: increase funding for healthcare research with appropriate emphasis on genomics; leverage India's assets such as traditional knowledge and genomic diversity in consultation with knowledge-holders; prioritize strategic entry points for India; improve industry-academic interface with appropriate incentives to improve public health and the nation's wealth; develop independent, accountable, transparent regulatory systems to ensure that ethical, legal and social issues are addressed for a single entry, smart and effective system; engage the public and ensure broad-based input into policy setting; ensure equitable access of poor to genomics products and services; deliver knowledge, products and services for public health. A key outcome of the course was the internet-based opinion leaders' network - the Indian Genome Policy Forum - a multi-stakeholder forum to foster further discussion on policy. CONCLUSION: We expect that the process that has led to this network will serve as a model to establish similar Science and Technology policy networks on regional levels and eventually on a global level.

Journal Article↗
PubMed1998

Noise-induced spiral waves in astrocyte syncytia show evidence of self-organized criticality.

Long range (a few centimeters), long lived (many seconds), spiral chemical waves of calcium ions (Ca2+) are observed in cultured networks of glial cells for normal concentrations of the neurotransmitter kainate. A new method for quantitatively measuring the spatiotemporal size of the waves is described. This measure results in a power law distribution of wave sizes, meaning that the process that creates the waves has no preferred spatial or temporal (size or lifetime) scale. This power law is one signature of self-organized critical phenomena, a class of behaviors found in many areas of science. The physiological results for glial networks are fully supported by numerical simulations of a simple network of noisy, communicating threshold elements. By contrast, waves observed in astrocytes cultured from human epileptic foci exhibited radically different behavior. The background random activity, or "noise", of the network is controlled by the kainate concentration. The mean rate of wave nucleation is mediated by the network noise. However, the power law distribution is invariant, within our experimental precision, over the range of noise intensities tested. These observations indicate that spatially and temporally coherent Ca2+ waves, mediated by network noise may play and important role in generating correlated neural activity (waves) over long distances and times in the healthy vertebrate central nervous system.

Astrocytes↗
PubMed2006

Derivation of the percolation threshold for the network model of Barabási and Albert.

The percolation threshold of the network model by Barabási and Albert [Science 286, 509 (1999)] (BA) has thus far only been "guessed" based on simulations and comparison with other models. Due to the still uncertain influence of correlations, the reference to other models cannot be justified. In this paper, we explicitly derive the well-known values for the BA model. To underline the importance of a null model like that of Barabási and Albert, we close with two basic remarks. First, we establish a connection between the abundance of scale-free networks in nature and the fact that power-law tails in the degree distribution result only from (at least asymptotically) linear preferential attachment: Only in the case of linear preferential attachment does a minimum of topological knowledge about the network suffice for the attachment process. Second, we propose a very simple and realistic extension of the BA model that accounts for clustering. We discuss the influence of clustering on the percolation properties.

Journal Article↗
PubMed2004

Trends in complexity theories and computation in the social sciences.

A modified bibliometric study and citation analysis of the use of complexity theories, encompassing chaos and complexity theory, and computational simulation in published literature was conducted. Articles published during 1971-1999 in four disciplines were examined: business, education, psychology and sociology. Overall, there was a marked pattern of increased use in the terms within the social sciences. There was a differentiated use of the terms between disciplines. A qualitative study on a subset from each discipline was generated to create a disciplinary profile of the quantitative and qualitative use of the terms in research activities, called a problem topology. Three research implications that arise from the differential adaptation of the theories and methods into the four social sciences are discussed.

Humans↗
PubMed2004

Terminological mapping for high throughput comparative biology of phenotypes.

Comparative biological studies have led to remarkable biomedical discoveries. While genomic science and technologies are advancing rapidly, our ability to precisely specify a phenotype and compare it to related phenotypes of other organisms remains challenging. This study has examined the systematic use of terminology and knowledge based technologies to enable high-throughput comparative phenomics. More specifically, we measured the accuracy of a multi-strategy automated classification method to bridge the phenotype gap between a phenotypic terminology (MGD: Phenoslim) and a broad-coverage clinical terminology (SNOMED CT). Furthermore, we qualitatively evaluate the additional emerging properties of the combined terminological network for comparative biology and discovery science. According to the gold standard (n = 100), the accuracies (precision / recall) of the composite automated methods were 67% / 97% (mapping for identical concepts) and 85% / 98% (classification). Quantitatively, only 2% of the phenotypic concepts were missing from the clinical terminology, however, qualitatively the gap was larger: conceptual scope, granularity and subtle yet significant, homonymy problems were observed. These results suggest that, as observed in other domains, additional strategies are required for combining terminologies.

Animals↗
PubMed1976

An experiment in teaching MEDLINE.

The design and implementation of an experimental MEDLINE module at Case Western Reserve University School of Library Science is described. The module places MEDLINE within the context of multiple data bases and networking operations andis embedded in a course in health science communications. The module consists of eight weeks of lectures, demonstrations, videotapes, and exercises on terminals.

Bibliography of Medicine↗
PubMedDate not supplied

Virtual shelves in a digital library: a framework for access to networked information sources.

OBJECTIVE: Develop a framework for collections-based access to networked information sources that addresses the problem of location-dependent access to information sources. DESIGN: This framework uses a metaphor of a virtual shelf. A virtual shelf is a general-purpose server that is dedicated to a particular information subject class. The identifier of one of these servers identifies its subject class. Location-independent call numbers are assigned to information sources. Call numbers are based on standard vocabulary codes. The call numbers are first mapped to the location-independent identifiers of virtual shelves. When access to an information resource is required, a location directory provides a second mapping of these location-independent server identifiers to actual network locations. RESULTS: The framework has been implemented in two different systems. One system is based on the Open System Foundation/Distributed Computing Environment and the other is based on the World Wide Web. CONCLUSIONS: This framework applies in new ways traditional methods of library classification and cataloging. It is compatible with two traditional styles of selecting information searching and browsing. Traditional methods may be combined with new paradigms of information searching that will be able to take advantage of the special properties of digital information. Cooperation between the library-informational science community and the informatics community can provide a means for a continuing application of the knowledge and techniques of library science to the new problems of networked information sources.

Catalogs, Library↗
PubMed1998

Architecture of a neural network client/server system for decision support in clinical information systems.

Neural networks have demonstrated their research potential in bio-medical sciences. The literature is filled with experiments that evidence the value of neural networks for the solution of diagnostic and predictive problems. The next step is to direct research efforts toward implementation of these neural networks into the clinical information environments where their diagnostic and predictive capabilities can be truly utilized. To accomplish this goal, it is necessary to create a new generation of neural network tools that ease the transition of experimental results to a production state. This paper describes the architecture of such a system.

Decision Support Systems, Clinical↗