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57
Structure and dynamics of molecular networks: A novel paradigm of drug discovery -- A . . .
- PHARMACOLOGY THERAPEUTICS
, 2013
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Cerebral: Visualizing Multiple Experimental Conditions on a Graph with Biological Context
, 2008
"... Abstract — Systems biologists use interaction graphs to model the behavior of biological systems at the molecular level. In an iterative process, such biologists observe the reactions of living cells under various experimental conditions, view the results in the context of the interaction graph, and ..."
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Cited by 35 (5 self)
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Abstract — Systems biologists use interaction graphs to model the behavior of biological systems at the molecular level. In an iterative process, such biologists observe the reactions of living cells under various experimental conditions, view the results in the context of the interaction graph, and then propose changes to the graph model. These graphs serve as a form of dynamic knowledge representation of the biological system being studied and evolve as new insight is gained from the experimental data. While numerous graph layout and drawing packages are available, these tools did not fully meet the needs of our immunologist collaborators. In this paper, we describe the data information display needs of these immunologists and translate them into design decisions. These decisions led us to create Cerebral, a system that uses a biologically guided graph layout and incorporates experimental data directly into the graph display. Small multiple views of different experimental conditions and a data-driven parallel coordinates view enable correlations between experimental conditions to be analyzed at the same time that the data is viewed in the graph context. This combination of coordinated views allows the biologist to view the data from many different perspectives simultaneously. To illustrate the typical analysis tasks performed, we analyze two datasets using Cerebral. Based on feedback from our collaborators we conclude that Cerebral is a valuable tool for analyzing experimental data in the context of an interaction graph model. Index Terms—Graph layout, systems biology visualization, small multiples, design study. 1
Statistical analysis strategies for association studies involving rare variants.
- Nature Reviews Genetics,
, 2010
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PHYLOViZ: phylogenetic inference and data visualization for sequence based typing methods
, 2012
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Interaction Techniques for Selecting and Manipulating Subgraphs in Network Visualizations
"... Abstract—We present a novel and extensible set of interaction techniques for manipulating visualizations of networks by selecting subgraphs and then applying various commands to modify their layout or graphical properties. Our techniques integrate traditional rectangle and lasso selection, and also ..."
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Cited by 17 (2 self)
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Abstract—We present a novel and extensible set of interaction techniques for manipulating visualizations of networks by selecting subgraphs and then applying various commands to modify their layout or graphical properties. Our techniques integrate traditional rectangle and lasso selection, and also support selecting a node’s neighbourhood by dragging out its radius (in edges) using a novel kind of radial menu. Commands for translation, rotation, scaling, or modifying graphical properties (such as opacity) and layout patterns can be performed by using a hotbox (a transiently popped-up, semi-transparent set of widgets) that has been extended in novel ways to integrate specification of commands with 1D or 2D arguments. Our techniques require only one mouse button and one keyboard key, and are designed for fast, gestural, in-place interaction. We present the design and integration of these interaction techniques, and illustrate their use in interactive graph visualization. Our techniques are implemented in NAViGaTOR, a software package for visualizing and analyzing biological networks. An initial usability study is also reported. Index Terms—Interactive graph drawing, network layout, radial menus, marking menus, hotbox, biological networks. 1
On Open Problems in Biological Network Visualization
, 2009
"... Much of the data generated and analyzed in the life sciences can be interpreted and represented by networks or graphs. Network analysis and visualization methods help in investigating them, and many universal as well as specialpurpose tools and libraries are available for this task. However, the two ..."
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Cited by 13 (6 self)
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Much of the data generated and analyzed in the life sciences can be interpreted and represented by networks or graphs. Network analysis and visualization methods help in investigating them, and many universal as well as specialpurpose tools and libraries are available for this task. However, the two fields of graph drawing and network biology are still largely disconnected. Hence, visualization of biological networks does typically not apply state-of-the-art graph drawing techniques, and graph drawing tools do not respect the drawing conventions of the life science community. In this paper, we analyze some of the major problems arising in biological network visualization. We characterize these problems and formulate a series of open graph drawing problems. These use cases illustrate the need for efficient algorithms to present, explore, evaluate, and compare biological network data. For each use case, problems are discussed and possible solutions suggested.
VisANT 4.0: Integrative network platform to connect genes, drugs, diseases and therapies. Nucleic acids research. 2013; 41:W225–231. doi:10.1093/nar/gkt401. [PubMed: 23716640
"... With the rapid accumulation of our knowledge on diseases, disease-related genes and drug targets, network-based analysis plays an increasingly import-ant role in systems biology, systems pharmacology and translational science. The new release of VisANT aims to provide new functions to facilitate the ..."
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Cited by 12 (1 self)
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With the rapid accumulation of our knowledge on diseases, disease-related genes and drug targets, network-based analysis plays an increasingly import-ant role in systems biology, systems pharmacology and translational science. The new release of VisANT aims to provide new functions to facilitate the con-venient network analysis of diseases, therapies, genes and drugs. With improved understanding of the mechanisms of complex diseases and drug ac-tions through network analysis, novel drug methods (e.g., drug repositioning, multi-target drug and com-bination therapy) can be designed. More specifically, the new update includes (i) integrated search and navigation of disease and drug hierarchies; (ii) integrated disease–gene, therapy–drug and drug–target association to aid the network construc-tion and filtering; (iii) annotation of genes/drugs using disease/therapy information; (iv) prediction of associated diseases/therapies for a given set of genes/drugs using enrichment analysis; (v) network transformation to support construction of versatile network of drugs, genes, diseases and therapies; (vi) enhanced user interface using docking windows to allow easy customization of node and edge properties with build-in legend node to distinguish different node type. VisANT is freely available at:
Epigenetic biomarker development
- Epigenomics
, 2009
"... genome-wide association studies ..."
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Visualizing genome expression and regulatory network dynamics in genomic and metabolic context. Computer Graphics Forum
"... Abstract DNA microarrays are used to measure the expression levels of thousands of genes simultaneously. In a time series experiment, the gene expressions are measured as a function of time. We present an application for integrated visualization of genome expression and network dynamics in both reg ..."
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Cited by 4 (2 self)
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Abstract DNA microarrays are used to measure the expression levels of thousands of genes simultaneously. In a time series experiment, the gene expressions are measured as a function of time. We present an application for integrated visualization of genome expression and network dynamics in both regulatory networks and metabolic pathways. Integration of these two levels of cellular processes is necessary, since it provides the link between the measurements at the transcriptional level (gene expression levels approximated from microarray data) and the phenotype (the observable characteristics of an organism) at the functional and behavioral level. The integration requires visualization approaches besides traditional clustering and statistical analysis methods. Our application can (i) visualize the data from time series experiments in the context of a regulatory network and KEGG metabolic pathways; (ii) identify and visualize active regulatory subnetworks from the gene expression data; (iii) perform a statistical test to identify and subsequently visualize pathways that are affected by differentially expressed genes. We present a case study, which demonstrates that our approach and application both facilitates and speeds up data analysis tremendously in comparison to a more traditional approach that involves many manual, laborious, and error-prone steps.
Visualizing temporal dynamics at the genomic and metabolic level
- In 13th Int. Conf. Information Visualization 2009
, 2009
"... We present an application for integrated visualization of gene expression data from time series experiments in gene regulation networks and metabolic networks. Such inte-gration is necessary, since it provides the link between the measurements at the transcriptional level and the observ-able charact ..."
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Cited by 4 (2 self)
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We present an application for integrated visualization of gene expression data from time series experiments in gene regulation networks and metabolic networks. Such inte-gration is necessary, since it provides the link between the measurements at the transcriptional level and the observ-able characteristics of an organism at the functional level. Our application can (i) visualize the data from time series experiments in the context of a regulatory network and a metabolic network; (ii) identify and visualize active regula-tory subnetworks from the gene expression data; (iii) per-form a statistical test to identify and subsequently visualize affected metabolic subnetworks. Initial results show that our integrated approach speeds up data analysis, and that it can reproduce results of a traditional approach that in-volves many manual and time-consuming steps. 1
