scholarly journals COMODI: An ontology to characterise differences in versions of computational models in biology

2016 ◽  
Author(s):  
Martin Scharm ◽  
Dagmar Waltemath ◽  
Pedro Mendes ◽  
Olaf Wolkenhauer
Keyword(s):  
Computational Biology ◽  
Simulation Study ◽  
Computational Models ◽  
Open Model ◽  
Semantic Level ◽  
Difference Detection ◽  
Model Repository ◽  
Error Corrections ◽  
Systems Models ◽  
Over Time

Motivation: Open model repositories provide ready-to-reuse computational models of biological systems. Models within those repositories evolve over time, leading to many alternative and subsequent versions. Taken together, the underlying changes reflect a model’s provenance and thus can give valuable insights into the studied biology. Currently, however, changes cannot be semantically interpreted. To improve this situation, we developed an ontology of terms describing changes in computational biology models. The ontology can be used by scientists and within software to characterise model updates at the level of single changes. When studying or reusing a model, these annotations help with determining the relevance of a change in a given context. Methods: We manually studied changes in selected models from BioModels and the Physiome Model Repository. Using the BiVeS tool for difference detection, we then performed an automatic analysis of changes in all models published in these repositories. The resulting set of concepts led us to define candidate terms for the ontology. In a final step, we aggregated and classified these terms and built the first version of the ontology. Results: We present COMODI, an ontology needed because COmputational MOdels DIffer. It empowers users and software to describe changes in a model on the semantic level. COMODI also enables software to implement user-specific filter options for the display of model changes. Finally, COMODI is the next step towards predicting how a change in a model influences the simulation study. Conclusion: COMODI, coupled with our algorithm for difference detection, ensures the transparency of a model’s evolution and it enhances the traceability of updates and error corrections. Availability: COMODI is encoded in OWL. It is openly available at http://comodi.sems.uni-rostock.de/.

scholarly journals COMODI: An ontology to characterise differences in versions of computational models in biology

2016 ◽  
Author(s):  
Martin Scharm ◽  
Dagmar Waltemath ◽  
Pedro Mendes ◽  
Olaf Wolkenhauer
Keyword(s):  
Computational Biology ◽  
Simulation Study ◽  
Computational Models ◽  
Open Model ◽  
Semantic Level ◽  
Difference Detection ◽  
Model Repository ◽  
Error Corrections ◽  
Systems Models ◽  
Over Time

Motivation: Open model repositories provide ready-to-reuse computational models of biological systems. Models within those repositories evolve over time, leading to many alternative and subsequent versions. Taken together, the underlying changes reflect a model’s provenance and thus can give valuable insights into the studied biology. Currently, however, changes cannot be semantically interpreted. To improve this situation, we developed an ontology of terms describing changes in computational biology models. The ontology can be used by scientists and within software to characterise model updates at the level of single changes. When studying or reusing a model, these annotations help with determining the relevance of a change in a given context. Methods: We manually studied changes in selected models from BioModels and the Physiome Model Repository. Using the BiVeS tool for difference detection, we then performed an automatic analysis of changes in all models published in these repositories. The resulting set of concepts led us to define candidate terms for the ontology. In a final step, we aggregated and classified these terms and built the first version of the ontology. Results: We present COMODI, an ontology needed because COmputational MOdels DIffer. It empowers users and software to describe changes in a model on the semantic level. COMODI also enables software to implement user-specific filter options for the display of model changes. Finally, COMODI is the next step towards predicting how a change in a model influences the simulation study. Conclusion: COMODI, coupled with our algorithm for difference detection, ensures the transparency of a model’s evolution and it enhances the traceability of updates and error corrections. Availability: COMODI is encoded in OWL. It is openly available at http://comodi.sems.uni-rostock.de/.

scholarly journals An algorithm to detect and communicate the differences in computational models describing biological systems

2014 ◽  
Author(s):  
Martin Scharm ◽  
Olaf Wolkenhauer ◽  
Dagmar Waltemath
Keyword(s):  
Biological Networks ◽  
Computational Models ◽  
Biological Systems ◽  
Biomodels Database ◽  
Difference Detection ◽  
Model Repository ◽  
Mathematical Expressions ◽  
Collaborative Modelling ◽  
History Of ◽  
Over Time

Repositories, such as the BioModels Database and the Physiome Model Repository support the reuse of models and ensure transparency about results in publications linked to those models. With thousands of models available, a framework to track the differences between models and their versions is essential to compare and combine models. Difference detection allows users to study the history of models but also helps in the detection of errors and inconsistencies. However, current repositories lack suitable methods to track a model’s development over time. Consequently, researchers have problems to grasp the differences between models and their versions. Focusing on SBML and CellML, we developed an algorithm to accurately detect and describe differences between versions of a model with respect to (i) the models’ encoding, (ii) the structure of biological networks, and (iii) mathematical expressions. Our method is implemented in a comprehensive and open library called BiVeS. Our work facilitates the reuse and extension of existing models. It also supports collaborative modelling. Finally, it contributes to better reproducibility of modelling results and to the challenge of model provenance. Our algorithm is the first tailor-made detector of differences between versions of computational models in standard formats.

scholarly journals An algorithm to detect and communicate the differences in computational models describing biological systems

2014 ◽  
Author(s):  
Martin Scharm ◽  
Olaf Wolkenhauer ◽  
Dagmar Waltemath
Keyword(s):  
Biological Networks ◽  
Computational Models ◽  
Biological Systems ◽  
Biomodels Database ◽  
Difference Detection ◽  
Model Repository ◽  
Mathematical Expressions ◽  
Collaborative Modelling ◽  
History Of ◽  
Over Time

Repositories, such as the BioModels Database and the Physiome Model Repository support the reuse of models and ensure transparency about results in publications linked to those models. With thousands of models available, a framework to track the differences between models and their versions is essential to compare and combine models. Difference detection allows users to study the history of models but also helps in the detection of errors and inconsistencies. However, current repositories lack suitable methods to track a model’s development over time. Consequently, researchers have problems to grasp the differences between models and their versions. Focusing on SBML and CellML, we developed an algorithm to accurately detect and describe differences between versions of a model with respect to (i) the models’ encoding, (ii) the structure of biological networks, and (iii) mathematical expressions. Our method is implemented in a comprehensive and open library called BiVeS. Our work facilitates the reuse and extension of existing models. It also supports collaborative modelling. Finally, it contributes to better reproducibility of modelling results and to the challenge of model provenance. Our algorithm is the first tailor-made detector of differences between versions of computational models in standard formats.

scholarly journals Extracting reproducible simulation studies from model repositories using the CombineArchive Toolkit

2015 ◽  
Author(s):  
Martin Scharm ◽  
Dagmar Waltemath
Keyword(s):  
Computational Biology ◽  
Simulation Study ◽  
Reproducible Research ◽  
Virtual Experiment ◽  
Graph Database ◽  
Simulation Studies ◽  
Valuable Resource ◽  
Open Model ◽  
Research Results ◽  
Biomodels Database

The COMBINE archive is a digital container format for files related to a virtual experiment in computational biology. It eases the management of numerous files related to a simulation study, fosters collaboration, and ultimately enables the exchange of reproducible research results. The CombineArchive Toolkit is a software for creating, exploring, modifying, and sharing COMBINE archives. Open model repositories such as BioModels Database are a valuable resource of models and associated simulation descriptions. However, so far no tool exists to export COMBINE archives for a given simulation study from such databases. Here we demonstrate how the CombineArchiveToolkit can be used to extract reproducible simulation studies from model repositories. We use the example of Masymos, a graph database with a sophisticated link concept to connect model-related files on the storage layer.

scholarly journals Extracting reproducible simulation studies from model repositories using the CombineArchive Toolkit

2015 ◽  
Author(s):  
Martin Scharm ◽  
Dagmar Waltemath
Keyword(s):  
Computational Biology ◽  
Simulation Study ◽  
Reproducible Research ◽  
Virtual Experiment ◽  
Graph Database ◽  
Simulation Studies ◽  
Valuable Resource ◽  
Open Model ◽  
Research Results ◽  
Biomodels Database

The COMBINE archive is a digital container format for files related to a virtual experiment in computational biology. It eases the management of numerous files related to a simulation study, fosters collaboration, and ultimately enables the exchange of reproducible research results. The CombineArchive Toolkit is a software for creating, exploring, modifying, and sharing COMBINE archives. Open model repositories such as BioModels Database are a valuable resource of models and associated simulation descriptions. However, so far no tool exists to export COMBINE archives for a given simulation study from such databases. Here we demonstrate how the CombineArchiveToolkit can be used to extract reproducible simulation studies from model repositories. We use the example of Masymos, a graph database with a sophisticated link concept to connect model-related files on the storage layer.

scholarly journals Glimpse: A Gaze-Based Measure of Temporal Salience

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3099
Author(s):  
V. Javier Traver ◽  
Judith Zorío ◽  
Luis A. Leiva
Keyword(s):  
Visual Attention ◽  
Computational Models ◽  
Temporal Evolution ◽  
Temporal Consistency ◽  
Visual Salience ◽  
Temporal Dimension ◽  
Spatial Perspective ◽  
Spatio Temporal ◽  
Scoring Algorithms ◽  
Over Time

Temporal salience considers how visual attention varies over time. Although visual salience has been widely studied from a spatial perspective, its temporal dimension has been mostly ignored, despite arguably being of utmost importance to understand the temporal evolution of attention on dynamic contents. To address this gap, we proposed Glimpse, a novel measure to compute temporal salience based on the observer-spatio-temporal consistency of raw gaze data. The measure is conceptually simple, training free, and provides a semantically meaningful quantification of visual attention over time. As an extension, we explored scoring algorithms to estimate temporal salience from spatial salience maps predicted with existing computational models. However, these approaches generally fall short when compared with our proposed gaze-based measure. Glimpse could serve as the basis for several downstream tasks such as segmentation or summarization of videos. Glimpse’s software and data are publicly available.

scholarly journals Potential of ontology for interoperability in e-government

2016 ◽  
Vol 10 (2) ◽  
Author(s):  
Edilson Ferneda ◽  
Fernando William Cruz ◽  
Hércules Antonio Do Prado ◽  
Renato da Veiga Guadagnin ◽  
Laurindo Campos Dos Santos ◽  
...  
Keyword(s):  
European Union ◽  
Electronic Government ◽  
Practical Solution ◽  
Semantic Level ◽  
Legal Question ◽  
Task Time ◽  
Efficient Mechanisms ◽  
Different Levels ◽  
Over Time ◽  
Legal Meaning

Interoperability is one of the fundamental requirements to enable electronic government. Its implementation can be classified into technical, syntactic, semantic, and organizational levels. At the semantic level, ontology is regarded as a practical solution to be considered. In this context, its adoption was identified in several countries, with different levels of maturity and so many focuses as the specific implementations. One of the main challenges to be overcome is the legal question that refers to the legislation to assure “the preservation of the legal meaning of data”. The lack of efficient mechanisms to support the deployment and use of ontologies can turn the overall task time-expensive, restricted in scope, or even unfeasible. Additionally, many initiatives are recent and need to be validated over time. This paper presents a non-exhaustive survey of the state of interoperability in e-government from the perspective of ontologies' use. The cases of Palestine, European Union, Netherlands, Estonia, and Brazil are discussed.

scholarly journals Decoding the Real-Time Neurobiological Properties of Incremental Semantic Interpretation

2020 ◽  
Vol 31 (1) ◽  
pp. 233-247
Author(s):  
Hun S Choi ◽  
William D Marslen-Wilson ◽  
Bingjiang Lyu ◽  
Billi Randall ◽  
Lorraine K Tyler
Keyword(s):  
Sentence Comprehension ◽  
Computational Models ◽  
Spoken Language ◽  
Semantic Interpretation ◽  
Semantic Constraints ◽  
Incremental Processing ◽  
Prior Context ◽  
Wide Range ◽  
Preceding Context ◽  
Over Time

Abstract Communication through spoken language is a central human capacity, involving a wide range of complex computations that incrementally interpret each word into meaningful sentences. However, surprisingly little is known about the spatiotemporal properties of the complex neurobiological systems that support these dynamic predictive and integrative computations. Here, we focus on prediction, a core incremental processing operation guiding the interpretation of each upcoming word with respect to its preceding context. To investigate the neurobiological basis of how semantic constraints change and evolve as each word in a sentence accumulates over time, in a spoken sentence comprehension study, we analyzed the multivariate patterns of neural activity recorded by source-localized electro/magnetoencephalography (EMEG), using computational models capturing semantic constraints derived from the prior context on each upcoming word. Our results provide insights into predictive operations subserved by different regions within a bi-hemispheric system, which over time generate, refine, and evaluate constraints on each word as it is heard.

Portable device design for in-vitro muscle tissue monitoring

2015 ◽  
Vol 82 (10) ◽  
Author(s):  
Dhouha Bouchaala ◽  
Mahdi Guermazi ◽  
Olfa Kanoun ◽  
Nabil Derbel
Keyword(s):  
Muscle Tissue ◽  
Portable Device ◽  
Phase Detector ◽  
Experimental Investigations ◽  
Frequency Range ◽  
Device Structure ◽  
Interface Circuit ◽  
Difference Detection ◽  
Over Time

AbstractExperimental investigations were carried out in order to identify the portable device requirements for in-vitro muscle tissue monitoring over time. Based on these investigations, specifications for the measurement system were defined considering the type and amplitude of excitation signals (500 mV), frequency range (1 kHz–10 MHz) and impedance range (10 Ω–4 kΩ). For these requirements, a portable device structure is proposed based on the magnitude ratio and phase difference detection using a gain phase detector. To fulfill the requirements of the gain phase detector circuit's inputs, an interface circuit is proposed with an error lower than 0.09% from 2 mV up to 200 mV.

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