Hierarchically organized layout for visualization of biochemical pathways

Tsay, Jyh-Jong; Wu, Bo-Liang; Jeng, Yu-Sen

doi:10.1016/j.artmed.2009.06.002

Cited by 13 publications

(8 citation statements)

References 10 publications

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“…Graph-based approaches were used for medical visualization, for instance, by Plaza et al [41]. Hierarchical visualization layouts were considered by Tsay et al [47]. Visualization for inference has been also investigated by Park et al [39] and Park and Huh [40].…”

Section: Resultsmentioning

confidence: 99%

An approach for Ewing test selection to support the clinical assessment of cardiac autonomic neuropathy

Stranieri

Abawajy

Kelarev

et al. 2013

Artificial Intelligence in Medicine

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The outcomes obtained can be used to determine the optimal sequences of tests for each individual cost-function by following the ODPF procedure. The results show that the best single Ewing test for diagnosing CAN is the deep breathing heart rate variation test. Optimal sequences found for the cost-function equal to the number of tests guarantee that the best accuracy is achieved after any number of tests and provide an improvement in comparison with the previous ordering of tests or a random sequence.

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Section: Resultsmentioning

confidence: 99%

An approach for Ewing test selection to support the clinical assessment of cardiac autonomic neuropathy

Stranieri

Abawajy

Kelarev

et al. 2013

Artificial Intelligence in Medicine

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“…Although this algorithm can appear trivial, it is widely used to visualize complexes and pathways [10]. …”

Section: Methodsmentioning

confidence: 99%

“…The main advantage of the Tree Layout algorithm is its low complexity (generally, it has linear performance in the number of nodes both for binary and n-ary trees) how demonstrated in [17] that makes it efficient and scalable, even with large graphs, but it presents a problem when arranging a huge number of nodes in limited areas as the monitor. Finally, the Tree Layout algorithm provides a natural way to visualize the global structure of a complex pathway [10]. …”

Section: Methodsmentioning

confidence: 99%

Visualization of protein interaction networks: problems and solutions

2013

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BackgroundVisualization concerns the representation of data visually and is an important task in scientific research. Protein-protein interactions (PPI) are discovered using either wet lab techniques, such mass spectrometry, or in silico predictions tools, resulting in large collections of interactions stored in specialized databases. The set of all interactions of an organism forms a protein-protein interaction network (PIN) and is an important tool for studying the behaviour of the cell machinery. Since graphic representation of PINs may highlight important substructures, e.g. protein complexes, visualization is more and more used to study the underlying graph structure of PINs. Although graphs are well known data structures, there are different open problems regarding PINs visualization: the high number of nodes and connections, the heterogeneity of nodes (proteins) and edges (interactions), the possibility to annotate proteins and interactions with biological information extracted by ontologies (e.g. Gene Ontology) that enriches the PINs with semantic information, but complicates their visualization.MethodsIn these last years many software tools for the visualization of PINs have been developed. Initially thought for visualization only, some of them have been successively enriched with new functions for PPI data management and PIN analysis. The paper analyzes the main software tools for PINs visualization considering four main criteria: (i) technology, i.e. availability/license of the software and supported OS (Operating System) platforms; (ii) interoperability, i.e. ability to import/export networks in various formats, ability to export data in a graphic format, extensibility of the system, e.g. through plug-ins; (iii) visualization, i.e. supported layout and rendering algorithms and availability of parallel implementation; (iv) analysis, i.e. availability of network analysis functions, such as clustering or mining of the graph, and the possibility to interact with external databases.ResultsCurrently, many tools are available and it is not easy for the users choosing one of them. Some tools offer sophisticated 2D and 3D network visualization making available many layout algorithms, others tools are more data-oriented and support integration of interaction data coming from different sources and data annotation. Finally, some specialistic tools are dedicated to the analysis of pathways and cellular processes and are oriented toward systems biology studies, where the dynamic aspects of the processes being studied are central.ConclusionA current trend is the deployment of open, extensible visualization tools (e.g. Cytoscape), that may be incrementally enriched by the interactomics community with novel and more powerful functions for PIN analysis, through the development of plug-ins. On the other hand, another emerging trend regards the efficient and parallel implementation of the visualization engine that may provide high interactivity and near real-time response time, as in NAViGaTOR. From a technological point...

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“…In this case, if the experiments confirm this relation, the algorithms can be used as predictors. Examples of algorithms for individuation of protein complexes are the Molecular Complex Detection algorithm (MCODE) 6 , described in the early work of Bader et al, [24], and The Markov Cluster algorithm (MCL) 7 [25,26] finds clusters on a graph by simulating a stochastic flow and then analyzing its distribution.…”

Section: Individuation Of Protein Complexesmentioning

confidence: 99%

“…• Circular Layout Algorithm [6]: how the name suggests, places the graph nodes in succession, one after the other, on a circle, so that all nodes are at the same distance from the center of the screen, It attempts to minimize overlapping vertices and cross edges (also named overlapping edges). Although this algorithm can appear trivial, it is widely used to visualize complexes and pathways .…”

Section: Visualization Of Graph-based Biomedical Datamentioning

confidence: 99%

Challenges and Opportunities for Visualization and Analysis of Graph-Modeled Medical Data

Agapito¹,

Guzzi²,

Cannataro³

2017

Preprint

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Graphs are largely used in computer science to model relations, or associations, among entities the compose complex systems. More recently, they found a broad field of application in bioinformatics and medical informatics supporting modelling, analysis of many systems. The applications span from representing interactions among molecules within cells, to model the functions of the brains. In order to support research, algorithms from graph theory that are able to extract knowledge from network should be coupled to efficient visualisation techniques. Although the importance of such topic, we retain that many challenges should be faced in the futures, such as automated pathway and network layout to better match biologists' needs, the developing of a standard able to give more and better choices to represent nodes, edges finally, the developing of automated mechanism able to improve the network navigation methods that help users manage large and complex networks with the goal to improve the usability.

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Hierarchically organized layout for visualization of biochemical pathways

Cited by 13 publications

References 10 publications

An approach for Ewing test selection to support the clinical assessment of cardiac autonomic neuropathy

An approach for Ewing test selection to support the clinical assessment of cardiac autonomic neuropathy

Visualization of protein interaction networks: problems and solutions

Challenges and Opportunities for Visualization and Analysis of Graph-Modeled Medical Data

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