72,525 research outputs found
A Monitoring Language for Run Time and Post-Mortem Behavior Analysis and Visualization
UFO is a new implementation of FORMAN, a declarative monitoring language, in
which rules are compiled into execution monitors that run on a virtual machine
supported by the Alamo monitor architecture.Comment: In M. Ronsse, K. De Bosschere (eds), proceedings of the Fifth
International Workshop on Automated Debugging (AADEBUG 2003), September 2003,
Ghent. cs.SE/030902
Rule-based Modeling of Cell Signaling: Advances in Model Construction, Visualization and Simulation
Rule-based modeling is a graph-based approach to specifying the kinetics of cell signaling
systems. A reaction rule is a compact and explicit graph-based representation of a kinetic process,
and it matches a class of reactions that involve identical sites and identical kinetics. Compact rule-
based models have been used to generate large and combinatorially complex reaction networks,
and rules have also been used to compile databases of kinetic interactions targeting specific cells
and pathways. In this work, I address three technological challenges associated with rule-based
modeling. First, I address the ability to generate an automated global visualization of a rule-based
model as a network of signal flows. I showed how to analyze a reaction rule and extract a set of
bipartite regulatory relationships, which can be aggregated across rules into a global network. I
also provide a set of coarse-graining approaches to compress an automatically generated network
into a compact pathway diagram, even for models with 100s of rules. Second, I resolved an
incompatibility between two recent advances in rule-based modeling: network-free simulation
(which enables simulation without generating a reaction network), and energy-based rule-based
modeling (which enables specifying a model using cooperativity parameters and automated
accounting of free energy). The incompatibility arose because calculating the reaction rate requires
computing the reaction free energy in an energy-based model, and this requires knowledge of both
reactants and products of the reaction, but the products are not available in a network-free
simulation until after the reaction event has fired. This was resolved by expanding each energy-
based rule into a number of normal reaction rules for which reaction free energies can be calculated
unambiguously. Third, I demonstrated a particular type of modularization that is based on treating
a set of rules as a module. This enables building models from combinations of modular hypotheses
and supplements the other modularization strategies such as macros, types and energy-based
compression
Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization
Motivation: Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model. Results: We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models. Although examples are given, using specific implementations the proposed techniques can be applied to rule-based models in general
Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization
Motivation: Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model. Results: We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models. Although examples are given, using specific implementations the proposed techniques can be applied to rule-based models in general. Availability and implementation: The annotation ontology for rule-based models can be found at http://purl.org/rbm/rbmo. The krdf tool and associated executable examples are available at http://purl.org/rbm/rbmo/krdf. Contact: or [email protected]
Visualization Techniques for Tongue Analysis in Traditional Chinese Medicine
Visual inspection of the tongue has been an important diagnostic method of Traditional Chinese Medicine (TCM). Clinic data have shown significant connections between various viscera cancers and abnormalities in the tongue and the tongue coating. Visual inspection of the tongue is simple and inexpensive, but the current practice in TCM is mainly experience-based and the quality of the visual inspection varies between individuals. The computerized inspection method provides quantitative models to evaluate color, texture and surface features on the tongue. In this paper, we investigate visualization techniques and processes to allow interactive data analysis with the aim to merge computerized measurements with human expert's diagnostic variables based on five-scale diagnostic conditions: Healthy (H), History Cancers (HC), History of Polyps (HP), Polyps (P) and Colon Cancer (C)
Construction safety and digital design: a review
As digital technologies become widely used in designing buildings and infrastructure, questions arise about
their impacts on construction safety. This review explores relationships between construction safety and
digital design practices with the aim of fostering and directing further research. It surveys state-of-the-art
research on databases, virtual reality, geographic information systems, 4D CAD, building information
modeling and sensing technologies, finding various digital tools for addressing safety issues in the
construction phase, but few tools to support design for construction safety. It also considers a literature on
safety critical, digital and design practices that raises a general concern about ‘mindlessness’ in the use of
technologies, and has implications for the emerging research agenda around construction safety and digital
design. Bringing these strands of literature together suggests new kinds of interventions, such as the
development of tools and processes for using digital models to promote mindfulness through multi-party
collaboration on safet
A framework for automated anomaly detection in high frequency water-quality data from in situ sensors
River water-quality monitoring is increasingly conducted using automated in
situ sensors, enabling timelier identification of unexpected values. However,
anomalies caused by technical issues confound these data, while the volume and
velocity of data prevent manual detection. We present a framework for automated
anomaly detection in high-frequency water-quality data from in situ sensors,
using turbidity, conductivity and river level data. After identifying end-user
needs and defining anomalies, we ranked their importance and selected suitable
detection methods. High priority anomalies included sudden isolated spikes and
level shifts, most of which were classified correctly by regression-based
methods such as autoregressive integrated moving average models. However, using
other water-quality variables as covariates reduced performance due to complex
relationships among variables. Classification of drift and periods of
anomalously low or high variability improved when we applied replaced anomalous
measurements with forecasts, but this inflated false positive rates.
Feature-based methods also performed well on high priority anomalies, but were
also less proficient at detecting lower priority anomalies, resulting in high
false negative rates. Unlike regression-based methods, all feature-based
methods produced low false positive rates, but did not and require training or
optimization. Rule-based methods successfully detected impossible values and
missing observations. Thus, we recommend using a combination of methods to
improve anomaly detection performance, whilst minimizing false detection rates.
Furthermore, our framework emphasizes the importance of communication between
end-users and analysts for optimal outcomes with respect to both detection
performance and end-user needs. Our framework is applicable to other types of
high frequency time-series data and anomaly detection applications
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