22,488 research outputs found
Exploring Case-Control Genetic Association Tests Using Phase Diagrams
Background: By a new concept called "phase diagram", we compare two commonly
used genotype-based tests for case-control genetic analysis, one is a
Cochran-Armitage trend test (CAT test at , or CAT0.5) and another
(called MAX2) is the maximization of two chi-square test results: one from the
two-by-two genotype count table that combines the baseline homozygotes and
heterozygotes, and another from the table that combines heterozygotes with risk
homozygotes. CAT0.5 is more suitable for multiplicative disease models and MAX2
is better for dominant/recessive models.
Methods: We define the CAT0.5-MAX2 phase diagram on the disease model space
such that regions where MAX2 is more powerful than CAT0.5 are separated from
regions where the CAT0.5 is more powerful, and the task is to choose the
appropriate parameterization to make the separation possible.
Results: We find that using the difference of allele frequencies ()
and the difference of Hardy-Weinberg disequilibrium coefficients
() can separate the two phases well, and the phase boundaries
are determined by the angle , which is an
improvement over the disease model selection using only.
Conclusions: We argue that phase diagrams similar to the one for CAT0.5-MAX2
have graphical appeals in understanding power performance of various tests,
clarifying simulation schemes, summarizing case-control datasets, and guessing
the possible mode of inheritance
A dynamical model of genetic networks describes cell differentiation
Cell differentiation is a complex phenomenon whereby a stem cell becomes progressively more specialized and eventually gives rise to a specific cell type. Differentiation can be either stochastic or, when appropriate signals are present, it can be driven to take a specific route. Induced pluripotency has also been recently obtained by overexpressing some genes in a differentiated cell. Here we show that a stochastic dynamical model of genetic networks can satisfactorily describe all these important features of differentiation, and others. The model is based on the emergent properties of generic genetic networks, it does not refer to specific control circuits and it can therefore hold for a wide class of lineages. The model points to a peculiar role of cellular noise in differentiation, which has never been hypothesized so far, and leads to non trivial predictions which could be subject to experimental testing
ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System
Security of computers and the networks that connect them is increasingly
becoming of great significance. Computer security is defined as the protection
of computing systems against threats to confidentiality, integrity, and
availability. There are two types of intruders: the external intruders who are
unauthorized users of the machines they attack, and internal intruders, who
have permission to access the system with some restrictions. Due to the fact
that it is more and more improbable to a system administrator to recognize and
manually intervene to stop an attack, there is an increasing recognition that
ID systems should have a lot to earn on following its basic principles on the
behavior of complex natural systems, namely in what refers to
self-organization, allowing for a real distributed and collective perception of
this phenomena. With that aim in mind, the present work presents a
self-organized ant colony based intrusion detection system (ANTIDS) to detect
intrusions in a network infrastructure. The performance is compared among
conventional soft computing paradigms like Decision Trees, Support Vector
Machines and Linear Genetic Programming to model fast, online and efficient
intrusion detection systems.Comment: 13 pages, 3 figures, Swarm Intelligence and Patterns (SIP)- special
track at WSTST 2005, Muroran, JAPA
Conceptual biology, hypothesis discovery, and text mining: Swanson's legacy
Innovative biomedical librarians and information specialists who want to expand their roles as expert searchers need to know about profound changes in biology and parallel trends in text mining. In recent years, conceptual biology has emerged as a complement to empirical biology. This is partly in response to the availability of massive digital resources such as the network of databases for molecular biologists at the National Center for Biotechnology Information. Developments in text mining and hypothesis discovery systems based on the early work of Swanson, a mathematician and information scientist, are coincident with the emergence of conceptual biology. Very little has been written to introduce biomedical digital librarians to these new trends. In this paper, background for data and text mining, as well as for knowledge discovery in databases (KDD) and in text (KDT) is presented, then a brief review of Swanson's ideas, followed by a discussion of recent approaches to hypothesis discovery and testing. 'Testing' in the context of text mining involves partially automated methods for finding evidence in the literature to support hypothetical relationships. Concluding remarks follow regarding (a) the limits of current strategies for evaluation of hypothesis discovery systems and (b) the role of literature-based discovery in concert with empirical research. Report of an informatics-driven literature review for biomarkers of systemic lupus erythematosus is mentioned. Swanson's vision of the hidden value in the literature of science and, by extension, in biomedical digital databases, is still remarkably generative for information scientists, biologists, and physicians. © 2006Bekhuis; licensee BioMed Central Ltd
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Coordinating visualizations of polysemous action: Values added for grounding proportion
We contribute to research on visualization as an epistemic learning tool by inquiring into the didactical potential of having students visualize one phenomenon in accord with two different partial meanings of the same concept. 22 Grade 4-6 students participated in a design study that investigated the emergence of proportional-equivalence notions from mediated perceptuomotor schemas. Working as individuals or pairs in tutorial clinical interviews, students solved non-symbolic interaction problems that utilized remote-sensing technology. Next, they used symbolic artifacts interpolated into the problem space as semiotic means to objectify in mathematical register a variety of both additive and multiplicative solution strategies. Finally, they reflected on tensions between these competing visualizations of the space. Micro-ethnographic analyses of episodes from three paradigmatic case studies suggest that students reconciled semiotic conflicts by generating heuristic logico-mathematical inferences that integrated competing meanings into cohesive conceptual networks. These inferences hinged on revisualizing additive elements multiplicatively. Implications are drawn for rethinking didactical design for proportions. © 2013 FIZ Karlsruhe
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