7,061 research outputs found

    A Bayesian method for evaluating and discovering disease loci associations

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    Background: A genome-wide association study (GWAS) typically involves examining representative SNPs in individuals from some population. A GWAS data set can concern a million SNPs and may soon concern billions. Researchers investigate the association of each SNP individually with a disease, and it is becoming increasingly commonplace to also analyze multi-SNP associations. Techniques for handling so many hypotheses include the Bonferroni correction and recently developed Bayesian methods. These methods can encounter problems. Most importantly, they are not applicable to a complex multi-locus hypothesis which has several competing hypotheses rather than only a null hypothesis. A method that computes the posterior probability of complex hypotheses is a pressing need. Methodology/Findings: We introduce the Bayesian network posterior probability (BNPP) method which addresses the difficulties. The method represents the relationship between a disease and SNPs using a directed acyclic graph (DAG) model, and computes the likelihood of such models using a Bayesian network scoring criterion. The posterior probability of a hypothesis is computed based on the likelihoods of all competing hypotheses. The BNPP can not only be used to evaluate a hypothesis that has previously been discovered or suspected, but also to discover new disease loci associations. The results of experiments using simulated and real data sets are presented. Our results concerning simulated data sets indicate that the BNPP exhibits both better evaluation and discovery performance than does a p-value based method. For the real data sets, previous findings in the literature are confirmed and additional findings are found. Conclusions/Significance: We conclude that the BNPP resolves a pressing problem by providing a way to compute the posterior probability of complex multi-locus hypotheses. A researcher can use the BNPP to determine the expected utility of investigating a hypothesis further. Furthermore, we conclude that the BNPP is a promising method for discovering disease loci associations. © 2011 Jiang et al

    Local Causal States and Discrete Coherent Structures

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    Coherent structures form spontaneously in nonlinear spatiotemporal systems and are found at all spatial scales in natural phenomena from laboratory hydrodynamic flows and chemical reactions to ocean, atmosphere, and planetary climate dynamics. Phenomenologically, they appear as key components that organize the macroscopic behaviors in such systems. Despite a century of effort, they have eluded rigorous analysis and empirical prediction, with progress being made only recently. As a step in this, we present a formal theory of coherent structures in fully-discrete dynamical field theories. It builds on the notion of structure introduced by computational mechanics, generalizing it to a local spatiotemporal setting. The analysis' main tool employs the \localstates, which are used to uncover a system's hidden spatiotemporal symmetries and which identify coherent structures as spatially-localized deviations from those symmetries. The approach is behavior-driven in the sense that it does not rely on directly analyzing spatiotemporal equations of motion, rather it considers only the spatiotemporal fields a system generates. As such, it offers an unsupervised approach to discover and describe coherent structures. We illustrate the approach by analyzing coherent structures generated by elementary cellular automata, comparing the results with an earlier, dynamic-invariant-set approach that decomposes fields into domains, particles, and particle interactions.Comment: 27 pages, 10 figures; http://csc.ucdavis.edu/~cmg/compmech/pubs/dcs.ht

    Evaluating Complex Social Interventions in a Complex World

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    The social world is complex and emergent. Inquiry, directed towards establishing universal empirical regularities (i.e. nomothetic inquiry), cannot establish causality in such a world. We can never assign a causal effect to any intervention without assessing the whole context of that intervention. However, we can develop generalizable knowledge if we adopt research approaches that recognize both the implications of assigning causal powers to context (the essence of the realist take on evaluation) and the significance of human agency in relation to ‘the social type of causal nexus’. There are literatures that can contribute to developing such knowledge. These include macro-political science’s concern with the importance of temporal ordering in relation to outcomes; Ragin’s set theoretic understanding of causal relations and his development of systematic comparison as a basis for explicating those relations through Qualitative Comparative Analysis (QCA); and the presentation of causal narratives as foundation for process tracing. Every complex social intervention has to be considered as a ‘case’. Systematic comparison across cases allows us to generalize within limits – but this still means we can transfer knowledge beyond the unique ideographically described instance. We can never establish universal/nomothetic accounts of causality in complex systems by using variable-based methods such as Randomized Controlled Trials (RCTs). However, through careful comparison and exploration of complex contingent causation, we can begin to get a handle on what works where (in what context), when (in what temporal context), and in what order

    The New Mechanical Philosophy

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    The New Mechanical Philosophy argues for a new image of nature and of science--one that understands both natural and social phenomena to be the product of mechanisms, and that casts the work of science as an effort to discover and understand those mechanisms. Drawing on an expanding literature on mechanisms in physical, life, and social sciences, Stuart Glennan offers an account of the nature of mechanisms and of the models used to represent them. A key quality of mechanisms is that they are particulars - located at different places and times, with no one just like another. The crux of the scientist\u27s challenge is to balance the complexity and particularity of mechanisms with our need for representations of them that are abstract and general.This volume weaves together metaphysical and methodological questions about mechanisms. Metaphysically, it explores the implications of the mechanistic framework for our understanding of classical philosophical questions about the nature of objects, properties, processes, events, causal relations, natural kinds and laws of nature. Methodologically, the book explores how scientists build models to represent and understand phenomena and the mechanisms responsible for them. Using this account of representation, Glennan offers a scheme for characterizing the enormous diversity of things that scientists call mechanisms, and explores the scope and limits of mechanistic explanation

    Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks

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    We present a procedure for effective estimation of entropy and mutual information from small-sample data, and apply it to the problem of inferring high-dimensional gene association networks. Specifically, we develop a James-Stein-type shrinkage estimator, resulting in a procedure that is highly efficient statistically as well as computationally. Despite its simplicity, we show that it outperforms eight other entropy estimation procedures across a diverse range of sampling scenarios and data-generating models, even in cases of severe undersampling. We illustrate the approach by analyzing E. coli gene expression data and computing an entropy-based gene-association network from gene expression data. A computer program is available that implements the proposed shrinkage estimator.Comment: 18 pages, 3 figures, 1 tabl

    Policy analysis: Evaluating theories of the hermeneutic critique

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    This thesis evaluates three theories representing the three perspectives identified by Goktug Morcol as perspectives of the hermeneutic critique in policy analysis, while explicating the revolutionary process that the science is currently undergoing. The evaluation is focused around the question as to whether the three theories are viable alternatives to positivist theories. First, a brief history of policy analysis is presented, highlighting the conditions that contributed to the rise of the general positivist paradigm as the ideal in policy analysis. This is followed by a summary and criticisms of the general positivist paradigm. Next, summaries of Dvora Yanow\u27s interpretive theory, Deborah Stone\u27s policy analysis as craft theory, and Frank Fischer\u27s discursive theory are presented as representatives of the three perspectives within the hermeneutic critique. Finally, an evaluation is offered of the three theories based on their ability to overcome the challenges presented by the criticisms of the general positivist paradigm
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