A Hybrid Forward Algorithm for RBF Neural Network Construction

This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well. In this study, it is achieved through simultaneous network structure determination and parameter optimization on the continuous parameter space. This is a mixed integer hard problem and the proposed HFA tackles this problem using an integrated analytic framework, leading to significantly improved network performance and reduced memory usage for the network construction. The computational complexity analysis confirms the efficiency of the proposed algorithm, and the simulation results demonstrate its effectiveness

Evaluation of predicted network modules in yeast metabolism using NMR-based metabolite profiling

Genome-scale metabolic models promise important insights into cell function. However, the definition of pathways and functional network modules within these models, and in the biochemical literature in general, is often based on intuitive reasoning. Although mathematical methods have been proposed to identify modules, which are defined as groups of reactions with correlated fluxes, there is a need for experimental verification. We show here that multivariate statistical analysis of the NMR-derived intra- and extracellular metabolite profiles of single-gene deletion mutants in specific metabolic pathways in the yeast Saccharomyces cerevisiae identified outliers whose profiles were markedly different from those of the other mutants in their respective pathways. Application of flux coupling analysis to a metabolic model of this yeast showed that the deleted gene in an outlying mutant encoded an enzyme that was not part of the same functional network module as the other enzymes in the pathway. We suggest that metabolomic methods such as this, which do not require any knowledge of how a gene deletion might perturb the metabolic network, provide an empirical method for validating and ultimately refining the predicted network structure.

Integrated structure selection and parameter optimisation for eng-genes neural models

The eng-genes concept involves the use of fundamental known system functions as activation functions in a neural model to create a 'grey-box' neural network. One of the main issues in eng-genes modelling is to produce a parsimonious model given a model construction criterion. The challenges are that (1) the eng-genes model in most cases is a heterogenous network consisting of more than one type of nonlinear basis functions, and each basis function may have different set of parameters to be optimised; (2) the number of hidden nodes has to be chosen based on a model selection criterion. This is a mixed integer hard problem and this paper investigates the use of a forward selection algorithm to optimise both the network structure and the parameters of the system-derived activation functions. Results are included from case studies performed on a simulated continuously stirred tank reactor process, and using actual data from a pH neutralisation plant. The resulting eng-genes networks demonstrate superior simulation performance and transparency over a range of network sizes when compared to conventional neural models. (c) 2007 Elsevier B.V. All rights reserved.

Stability in a Network Economy: The Role of Institutions

We consider an economy in which agents are embedded in a network of potential value-generating relationships. Agents are assumed to be able to participate in three types of economic interactions: Autarkic self-provision; bilateral interaction; and multilateral collaboration through endogenously provided platforms.
We introduce two stability concepts and provide sufficient and necessary conditions on the network structure that guarantee existence, in cases of the absence of externalities, link-based externalities and crowding externalities. We show that institutional arrangements based on socioeconomic roles and leadership guarantee stability. In particular, the stability of more complex economic outcomes requires more strict and complex institutional rules to govern economic interactions. We investigate strict social hierarchies, tiered leadership structures and global market places.

Network signatures based on gene pair expression ratios improve classification and the analysis of muscle-invasive urothelial cancer

Urothelial cancer (UC) is highly recurrent and can progress from non-invasive (NMIUC) to a more aggressive muscle-invasive (MIUC) subtype that invades the muscle tissue layer of the bladder. We present a proof of principle study that network-based features of gene pairs can be used to improve classifier performance and the functional analysis of urothelial cancer gene expression data. In the first step of our procedure each individual sample of a UC gene expression dataset is inflated by gene pair expression ratios that are defined based on a given network structure. In the second step an elastic net feature selection procedure for network-based signatures is applied to discriminate between NMIUC and MIUC samples. We performed a repeated random subsampling cross validation in three independent datasets. The network signatures were characterized by a functional enrichment analysis and studied for the enrichment of known cancer genes. We observed that the network-based gene signatures from meta collections of proteinprotein interaction (PPI) databases such as CPDB and the PPI databases HPRD and BioGrid improved the classification performance compared to single gene based signatures. The network based signatures that were derived from PPI databases showed a prominent enrichment of cancer genes (e.g., TP53, TRIM27 and HNRNPA2Bl). We provide a novel integrative approach for large-scale gene expression analysis for the identification and development of novel diagnostical targets in bladder cancer. Further, our method allowed to link cancer gene associations to network-based expression signatures that are not observed in gene-based expression signatures.

An examination of the rheological and mucoadhesive properties of poly(Acrylic acid) organogels designed as platforms for local drug delivery to the oral cavity

This study examined the rheological/mucoadhesive properties of poly (acrylic acid) PAA organogels as platforms for drug delivery to the oral cavity. Organogels were prepared using PAA (3%, 5%, 10% w/w) dissolved in ethylene glycol (EG), propylene glycol (PG), 1,3-propylene glycol (1,3-PG), 1,5-propanediol (1,5-PD), polyethylene glycol 400 (PEG 400), or glycerol. All organogels exhibited pseudoplastic flow. The increase in storage (G') and loss (G '') moduli of organogels as a function of frequency was minimal, G '' was greater than G '' (at all frequencies), and the loss tangent <1, indicative of gel behavior. Organogels prepared using EG, PG, and 1,3-propanediol (1,3-PD) exhibited similar flow/viscoelastic properties. Enhanced rheological structuring was associated with organogels prepared using glycerol (in particular) and PEG 400 due to their interaction with adjacent carboxylic acid groups on each chain and on adjacent chains. All organogels (with the exception of 1,5-PD) exhibited greater network structure than aqueous PAA gels. Organogel mucoadhesion increased with polymer concentration. Greatest mucoadhesion was associated with glycerol-based formulations, whereas aqueous PAA gels exhibited the lowest mucoadhesion. The enhanced network structure and the excellent mucoadhesive properties of these organogels, both of which may be engineered through choice of polymer concentration/solvent type, may be clinically useful for the delivery of drugs to the oral cavity.

Translating evidence into practice: A shared priority in public health?

Translational and transdisciplinary research is needed to tackle complex public health problems. This article has three aims. Firstly, to determine how academics and non-academics (practitioners, policy makers and community workers) identified with the goals of the UKCRC Centre of Excellence for Public Health in Northern Ireland and how their attitudes varied in terms of knowledge brokerage and translation. Secondly, to map and analyse the network structure of the public health sector and the placement of the Centre within this. Thirdly, to aggregate responses from members of the network by work setting to construct the trans-sectoral network and devise the Root Mean Sum of Squares to determine the quality and potential value of connections across this network.The analysis was based on data collected from 98 individuals who attended the launch of the Centre in June 2008. Analysis of participant expectations and personal goals suggests that the academic members of the network were more likely to expect the work of the Centre to produce new knowledge than non-academics, but less likely to expect the Centre to generate health interventions and influence health policy. Academics were also less strongly oriented than non-academics to knowledge transfer as a personal goal, though more confident that research findings would be diffused beyond the immediate network. A central core of five nodes is crucial to the overall configuration of the regional public health network in Northern Ireland, with the Centre being well placed to exert influence within this. Though the overall network structure is fairly robust, the connections between some component parts of the network - such as academics and the third sector - are unidirectional.Identifying these differences and core network structure is key to translational and transdisciplinary research. Though exemplified in a regional study, these techniques are generalisable and applicable to many networks of interest: public health, interdisciplinary research or organisational involvement and stakeholder linkage.

Universal power-law diet partitioning by marine fish and squid with surprising stability–diversity implications

A central question in community ecology is how the number of trophic links relates to community species richness. For simple dynamical food-web models, link density (the ratio of links to species) is bounded from above as the number of species increases; but empirical data suggest that it increases without bounds. We found a new empirical upper bound on link density in large marine communities with emphasis on fish and squid, using novel methods that avoid known sources of bias in traditional approaches. Bounds are expressed in terms of the diet-partitioning function (DPF): the average number of resources contributing more than a fraction f to a consumer's diet, as a function of f. All observed DPF follow a functional form closely related to a power law, with power-law exponents indepen- dent of species richness at the measurement accuracy. Results imply universal upper bounds on link density across the oceans. However, the inherently scale-free nature of power-law diet partitioning suggests that the DPF itself is a better defined characterization of network structure than link density.

Automated nonlinear system modelling with multiple neural networks

This article discusses the identification of nonlinear dynamic systems using multi-layer perceptrons (MLPs). It focuses on both structure uncertainty and parameter uncertainty, which have been widely explored in the literature of nonlinear system identification. The main contribution is that an integrated analytic framework is proposed for automated neural network structure selection, parameter identification and hysteresis network switching with guaranteed neural identification performance. First, an automated network structure selection procedure is proposed within a fixed time interval for a given network construction criterion. Then, the network parameter updating algorithm is proposed with guaranteed bounded identification error. To cope with structure uncertainty, a hysteresis strategy is proposed to enable neural identifier switching with guaranteed network performance along the switching process. Both theoretic analysis and a simulation example show the efficacy of the proposed method.

Characterization of Transmission Losses

In this paper, characterizing transmission losses according to their origin is carried out. Transmission loss is decomposed into three components. The first is due to the current flow from generators to loads. The second is due to the circulating current between generators. The third represents the contribution of network structure and controls to increasing or decreasing transmission losses. Analytical proofs of the proposed loss decomposition are presented along with methods of allocating each component to the parties contributing to it. Illustration on simple dc and ac systems is presented. Results of application of the proposed method compared with other methods are also presented.

The predictive science of community ecology

Body mass measures provide a tantalizing tool for explaining both variation in emergent community-level patterns and as a mechanistic basis for fundamental processes such as metabolism, consumption and competition. The unification of body mass, abundance and food web (ecological network) structure in community ecology is an effective way to explore future scenarios of environmental change. However, constraints over the availability of data against which to validate model predictions limit the application of size-based approaches. Here, I explore issues over the use of body size for predicting interaction strengths and hence the dynamics of natural ecosystems. The advantages, disadvantages, opportunities and limitations of such approaches are explored. © 2011 The Author. Journal of Animal Ecology © 2011 British Ecological Society.

Urothelial cancer gene regulatory networks inferred from large-scale RNAseq, Bead and Oligo gene expression data

BACKGROUND: Urothelial pathogenesis is a complex process driven by an underlying network of interconnected genes. The identification of novel genomic target regions and gene targets that drive urothelial carcinogenesis is crucial in order to improve our current limited understanding of urothelial cancer (UC) on the molecular level. The inference of genome-wide gene regulatory networks (GRN) from large-scale gene expression data provides a promising approach for a detailed investigation of the underlying network structure associated to urothelial carcinogenesis.

METHODS: In our study we inferred and compared three GRNs by the application of the BC3Net inference algorithm to large-scale transitional cell carcinoma gene expression data sets from Illumina RNAseq (179 samples), Illumina Bead arrays (165 samples) and Affymetrix Oligo microarrays (188 samples). We investigated the structural and functional properties of GRNs for the identification of molecular targets associated to urothelial cancer.

RESULTS: We found that the urothelial cancer (UC) GRNs show a significant enrichment of subnetworks that are associated with known cancer hallmarks including cell cycle, immune response, signaling, differentiation and translation. Interestingly, the most prominent subnetworks of co-located genes were found on chromosome regions 5q31.3 (RNAseq), 8q24.3 (Oligo) and 1q23.3 (Bead), which all represent known genomic regions frequently deregulated or aberated in urothelial cancer and other cancer types. Furthermore, the identified hub genes of the individual GRNs, e.g., HID1/DMC1 (tumor development), RNF17/TDRD4 (cancer antigen) and CYP4A11 (angiogenesis/ metastasis) are known cancer associated markers. The GRNs were highly dataset specific on the interaction level between individual genes, but showed large similarities on the biological function level represented by subnetworks. Remarkably, the RNAseq UC GRN showed twice the proportion of significant functional subnetworks. Based on our analysis of inferential and experimental networks the Bead UC GRN showed the lowest performance compared to the RNAseq and Oligo UC GRNs.

CONCLUSION: To our knowledge, this is the first study investigating genome-scale UC GRNs. RNAseq based gene expression data is the data platform of choice for a GRN inference. Our study offers new avenues for the identification of novel putative diagnostic targets for subsequent studies in bladder tumors.

Learning Bounded Tree-Width Bayesian Networks via Sampling

Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods [12, 14] tackle the problem by using k-trees to learn the optimal Bayesian network with tree-width up to k. In this paper, we propose a sampling method to efficiently find representative k-trees by introducing an Informative score function to characterize the quality of a k-tree. The proposed algorithm can efficiently learn a Bayesian network with tree-width at most k. Experiment results indicate that our approach is comparable with exact methods, but is much more computationally efficient.

Cross-section and panel estimates of peer effects in early adolescent cannabis use: With a little help from my ‘friends once removed’

Peer effects in adolescent cannabis are difficult to estimate, due in part to the lack of appropriate data on behaviour and social ties. This paper exploits survey data that have many desirable properties and have not previously been used for this purpose. The data set, collected from teenagers in three annual waves from 2002-2004 contains longitudinal information about friendship networks within schools (N = 5,020). We exploit these data on network structure to estimate peer effects on adolescents from their nominated friends within school using two alternative approaches to identification. First, we present a cross-sectional instrumental variable (IV) estimate of peer effects that exploits network structure at the second degree, i.e. using information on friends of friends who are not themselves ego’s friends to instrument for the cannabis use of friends. Second, we present an individual fixed effects estimate of peer effects using the full longitudinal structure of the data. Both innovations allow a greater degree of control for correlated effects than is commonly the case in the substance-use peer effects literature, improving our chances of obtaining estimates of peer effects than can be plausibly interpreted as causal. Both estimates suggest positive peer effects of non-trivial magnitude, although the IV estimate is imprecise. Furthermore, when we specify identical models with behaviour and characteristics of randomly selected school peers in place of friends’, we find effectively zero effect from these ‘placebo’ peers, lending credence to our main estimates. We conclude that cross-sectional data can be used to estimate plausible positive peer effects on cannabis use where network structure information is available and appropriately exploited.