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PubMed2002

Network motifs: simple building blocks of complex networks.

Complex networks are studied across many fields of science. To uncover their structural design principles, we defined "network motifs," patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in randomized networks. We found such motifs in networks from biochemistry, neurobiology, ecology, and engineering. The motifs shared by ecological food webs were distinct from the motifs shared by the genetic networks of Escherichia coli and Saccharomyces cerevisiae or from those found in the World Wide Web. Similar motifs were found in networks that perform information processing, even though they describe elements as different as biomolecules within a cell and synaptic connections between neurons in Caenorhabditis elegans. Motifs may thus define universal classes of networks. This approach may uncover the basic building blocks of most networks.

Algorithms↗
PubMed2005

The 'geneticisation' of heart disease: a network analysis of the production of new genetic knowledge.

Genetic science is making ever-expanding claims about the (mal)functioning of the body. The 'geneticisation' of health and medicine is extending from rare single gene conditions to more common multi-factorial disease, such as heart disease. The dominant behavioural and socio-spatial explanations of heart disease are now being challenged by genetic claims of deterministic biological causes. This paper builds an account of the transformation of heart disease in the new genetics era, by applying actor network theory (ANT) to the production of genetic knowledge of one aspect of heart disease-hypertension-within a medical genetics laboratory in Glasgow, Scotland. Using this approach, the paper shows that there is no straightforward geneticisation of heart disease. Instead, there is a contested, complex and uncertain understanding of heart disease as genetic, a product of the many people, technologies, natural elements and spaces involved in the network of genetic science knowledge making. The paper concludes that a 'critical' ANT could be developed that acknowledges the inherent unevenness of the network, and connects genetic and socio-spatial explanations of heart disease.

Causality↗
PubMed2006

Nanomedicine-emerging or re-emerging ethical issues? A discussion of four ethical themes.

Nanomedicine plays a prominent role among emerging technologies. The spectrum of potential applications is as broad as it is promising. It includes the use of nanoparticles and nanodevices for diagnostics, targeted drug delivery in the human body, the production of new therapeutic materials as well as nanorobots or nanoprotheses. Funding agencies are investing large sums in the development of this area, among them the European Commission, which has launched a large network for life-sciences related nanotechnology. At the same time government agencies as well as the private sector are putting forward reports of working groups that have looked into the promises and risks of these developments. This paper will begin with an introduction to the central ethical themes as identified by selected reports from Europe and beyond. In a next step, it will analyse the most frequently invoked ethical concerns-risk assessment and management, the issues of human identity and enhancement, possible implications for civil liberties (e.g. nanodevices that might be used for covert surveillance), and concerns about equity and fair access. Although it seems that the main ethical issues are not unique to nanotechnologies, the conclusion will argue against shrugging them off as non-specific items that have been considered before in the context of other biomedical technologies, such as gene therapy or xenotransplantation. Rather, the paper will call on ethicists to help foster a rational, fair and participatory discourse on the different potential applications of nanotechnologies in medicine, which can form the basis for informed and responsible societal and political decisions.

Biomedical Technology↗
PubMed1996

On Temporal Generalization of Simple Recurrent Networks.

Simple recurrent networks (Elman networks) have been widely used in temporal processing applications. In this study we investigate temporal generalization of simple recurrent networks, drawing comparisons between network capabilities and human performance. Elman networks are trained to generate temporal trajectories sampled at different rates. The networks are then tested with trajectories at the trained rates and other sampling rates, including trajectories representing mixtures of different sampling rates. It is found that for simple trajectories the networks show interval invariance, but not rate invariance. However, for complex trajectories which require greater contextural information, these networks do not seem to show any temporal generalization. Similar results are also obtained using measured speech data. These results suggest that this class of recurrent networks exhibits severe limitations in temporal generalization. Discussions are provided regarding rate invariance and possible ways to achieve it in neural networks. Copyright 1996 Elsevier Science Ltd

Journal Article↗
PubMed1996

On the Circuit Complexity of Sigmoid Feedforward Neural Networks.

This paper aims to examine the circuit complexity of sigmoid activation feedforward artificial neural networks by placing them amongst several classic Boolean and threshold gate circuit complexity classes. The starting point is the class NN(k) defined by [Shawe-Taylor et al. (1992)] Classes of feedforward neural nets and their circuit complexity. Neural Networks 5(6), 971-977. For a better characterisation, we introduce two additional classes NN(k)(Delta) and NN(k)(Delta,epsilon) having less restrictive conditions than NN(k) concerning fan-in and accuracy, and proceed to prove relations amongst these three classes and well established circuit complexity classes. For doing that, a particular class of Boolean functions F(Delta) is first introduced and we show how a threshold gate circuit can be recursively built for any f(Delta) belonging to F(Delta). As the G-functions (computing the carries) are f(Delta) functions, a class of solutions is obtained for threshold gate adders. We then constructively prove the inclusions amongst circuit complexity classes. This is done by converting the sigmoid feedforward artificial neural network into an equivalent threshold gate circuit [Shawe-Taylor et al. (1992)]. Each threshold gate is then replaced by a multiple input adder having a binary tree structure, relaxing the logarithmic fan-in condition from ([Shawe-Taylor et al. 1992]) to (almost) polynomial. This means that larger classes of sigmoid activation feedforward neural networks can be implemented in polynomial size Boolean circuits with a small constant fan-in at the expense of a logarithmic factor increase in the number of layers. Similar results are obtained for threshold circuits, and are liked with the previous ones. The main conclusion is that there are interesting fan-in dependent depth-size tradeoffs when trying to digitally implement sigmoid activation feedforward neural networks. Copyright 1996 Elsevier Science Ltd

Journal Article↗
PubMed1997

PROforma: a general technology for clinical decision support systems.

The need for flexible and well understood knowledge representations which are capable of capturing clinical guidelines and protocols for decision support systems is widely recognised. The PROforma method for specifying clinical guidelines and protocols comprises a graphical notation for their design, and a formal knowledge representation language to enable them to be executed by a computer to support the management of medical procedures and clinical decision making. PROforma technology consists of a graphical knowledge editor for the creation of guidelines, and an enactment engine for testing and executing them. This paper provides an overview of the motivation and structure of PROforma, and illustrates its use in the development of clinical applications.

Adult↗
PubMed2004

Web-based interventions for substance use disorders: a qualitative review.

Substance use disorder is one of the most common mental health problems in the Western world with a significant contribution to the global burden of disease and a high level of unmet treatment need. To assess the use and effectiveness of web-based interventions for substance use disorders. A qualitative review of the published literature across databases Medline, EMBASE, PsychINFO, GrayLIT Network, and Web of Science using relevant key terms. A search of the worldwide web was also conducted using search engines such as Google. There were a number of computerized and internet-based interventions for mental health disorders including substance use disorders located; however, they are largely descriptive with no large randomized controlled trials of internet-delivered interventions for substance use disorders reported. While the literature on internet-based substance use interventions is sparse and flawed, the potential impact of effective intervention is considerable. On the basis of the limited research available it is reasonable to suggest that a demand for such interventions exists and there is a likelihood that they would be as effective as those delivered by therapists for the majority of less severely dependent clients. Further clinical outcome research, particularly in the area of brief interventions for alcohol use disorders and extension to other drugs such as cannabis and club drugs, is certainly justified. (c) 2004 Elsevier Science Inc. All rights reserved.

Health Education↗
PubMed1996

Object Generation with Neural Networks (When Spurious Memories are Useful).

Object generation constitutes a new class of problems which can be solved using neural networks. It is reverse to classification and makes a good use of the stable network modes generally considered as undesired (spurious memories). Single-class networks are considered as basic elements instead of multiclass ones, and attractors of the former are treated as potential objects of the corresponding class. Development of multiple attractors reflects the network generalization abilities and converts the single-class network into the active generator of templates. Object generating networks can also be used as moduli of recognition systems which are free from some disadvantages inherent in multi-class discriminant networks. Copyright 1996 Elsevier Science Ltd.

Journal Article↗
PubMed1997

Training Feedforward Neural Networks: An Algorithm Giving Improved Generalization.

An algorithm is derived for supervised training in multilayer feedforward neural networks. Relative to the gradient descent backpropagation algorithm it appears to give both faster convergence and improved generalization, whilst preserving the system of backpropagating errors through the network. Copyright 1996 Elsevier Science Ltd.

Journal Article↗
PubMed1997

Optimal Linear Combinations of Neural Networks.

Neural network-based modeling often involves trying multiple networks with different architectures and training parameters in order to achieve acceptable model accuracy. Typically, one of the trained networks is chosen as best, while the rest are discarded. [Hashem and Schmeiser (1995)] proposed using optimal linear combinations of a number of trained neural networks instead of using a single best network. Combining the trained networks may help integrate the knowledge acquired by the components networks and thus improve model accuracy. In this paper, we extend the idea of optimal linear combinations (OLCs) of neural networks and discuss issues related to the generalization ability of the combined model. We then present two algorithms for selecting the component networks for the combination to improve the generalization ability of OLCs. Our experimental results demonstrate significant improvements in model accuracy, as a result of using OLCs, compared to using the apparent best network. Copyright 1997 Elsevier Science Ltd.

Journal Article↗
PubMed1997

Genetically Trained Cellular Neural Networks.

Real-coded genetic algorithms on a parallel architecture are applied to optimize the synaptic couplings of a Cellular Neural Network for specific greyscale image processing tasks. Using supervised learning information in the fitness function, we propose the Genetic Algorithm as a general training method for Cellular Neural Networks. Copyright 1997 Elsevier Science Ltd.

Journal Article↗
PubMed1999

Estimating the number of clusters in multivariate data by self-organizing maps.

Determining the structure of data without prior knowledge of the number of clusters or any information about their composition is a problem of interest in many fields, such as image analysis, astrophysics, biology, etc. Partitioning a set of n patterns in a p-dimensional feature space must be done such that those in a given cluster are more similar to each other than the rest. As there are approximately Kn/K! possible ways of partitioning the patterns among K clusters, finding the best solution is very hard when n is large. The search space is increased when we have no a priori number of partitions. Although the self-organizing feature map (SOM) can be used to visualize clusters, the automation of knowledge discovery by SOM is a difficult task. This paper proposes region-based image processing methods to post-processing the U-matrix obtained after the unsupervised learning performed by SOM. Mathematical morphology is applied to identify regions of neurons that are similar. The number of regions and their labels are automatically found and they are related to the number of clusters in a multivariate data set. New data can be classified by labeling it according to the best match neuron. Simulations using data sets drawn from finite mixtures of p-variate normal densities are presented as well as related advantages and drawbacks of the method.

Algorithms↗
PubMed2007

2nd Ophthalmic Drug Development and Delivery Summit.

The Second Annual Ophthalmic Drug Development and Delivery Summit was held on 19 - 20 September 2006 in San Diego, CA, US. The 2-day symposium, having a highly focused theme, was packed with cutting-edge science, insightful overviews and networking opportunities. With a total of 11 recognized specialists presenting reviews and recent results in the advancement of ocular drug development and delivery, the invited expert speaking faculty presented the latest preclinical and clinical developments in novel ophthalmic therapies and drug delivery technology. The talks included various case studies from primary investigators and pharmaceutical companies touching upon key topics: updates on current clinical trials, study design issues, sustained delivery to the eye, views of the vitreous space as a drug reservoir, new developments in dry and wet age-related macular degeneration and diabetic retinopathy, formulation for optimal drug delivery, differences and similarities in developing drugs for the eye compared with other targets, pharmacokinetics, novel ocular delivery methods and devices, delivery of proteins and peptides, focal drug delivery, non-invasive drug delivery to the eye, neuroprotection challenges, in vitro and in vivo models for glaucoma and angiogenesis for early efficacy estimation, and toxicology. Overall, the 2-day annual symposium continues to grow as an efficient platform for fostering discussion on a range of scientific topics and challenges and avenues for building collaborative partnerships in ophthalmic drug development.

Animals↗