ReactionFlow: an interactive visualization tool for causality analysis in biological pathways

Dang, Tommy; Murray, Paul; Aurisano, Jillian; Forbes, Angus G.

doi:10.1186/1753-6561-9-s6-s6

Cited by 39 publications

(35 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cytoscape is the most popular tool in biology and counts with many plugins for Systems Biology in particular, including two that work with cellular partitions: Cerebral [7] and Mosaic [8]. Other software systems, like Extended LineSets [9], Entourage [10], and Reaction-Flow [11], focus on the analysis of pathways and their mechanisms.…”

Section: Introductionmentioning

confidence: 99%

CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components

Heberle

Carazzolle

Telles

et al. 2017

Preprint

View full text Add to dashboard Cite

Background: The advent of "omics" science has brought new perspectives in contemporary biology through the high-throughput analyses of molecular interactions, providing new clues in protein/gene function and in the organization of biological pathways. Biomolecular interaction networks, or graphs, are simple abstract representations where the components of a cell (e.g. proteins, metabolites etc.) are represented by nodes and their interactions are represented by edges. An appropriate visualization of data is crucial for understanding such networks, since pathways are related to functions that occur in specific regions of the cell. The force-directed layout is an important and widely used technique to draw networks according to their topologies. Placing the networks into cellular compartments helps to quickly identify where network elements are located and, more specifically, concentrated. Currently, only a few tools provide the capability of visually organizing networks by cellular compartments. Most of them cannot handle large and dense networks. Even for small networks with hundreds of nodes the available tools are not able to reposition the network while the user is interacting, limiting the visual exploration capability. Results: Here we propose CellNetVis, a web tool to easily display biological networks in a cell diagram employing a constrained force-directed layout algorithm. The tool is freely available and open-source. It was originally designed for networks generated by the Integrated Interactome System and can be used with networks from others databases, like InnateDB. Conclusions: CellNetVis has demonstrated to be applicable for dynamic investigation of complex networks over a consistent representation of a cell on the Web, with capabilities not matched elsewhere.

show abstract

Section: Introductionmentioning

confidence: 99%

CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components

Heberle

Carazzolle

Telles

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Within their interface, the authors designed a slider bar that allows users to filter the edges corresponding to weak relations. ReactionFlow [18] was developed to facilitate a better understanding of causal relationships between proteins and biochemical reactions in biological pathways. It organizes the causal pathways into a Sankey-based structure to emphasize the downstream and upstream nature of the causal relationships.…”

Section: Visual Causality Analysismentioning

confidence: 99%

Visual Progression Analysis of Event Sequence Data

Guo

Jin

Gotz

et al. 2019

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Event sequence data is common to a broad range of application domains, from security to health care to scholarly communication. This form of data captures information about the progression of events for an individual entity (e.g., a computer network device; a patient; an author) in the form of a series of time-stamped observations. Moreover, each event is associated with an event type (e.g., a computer login attempt, or a hospital discharge). Analyses of event sequence data have been shown to help reveal important temporal patterns, such as clinical paths resulting in improved outcomes, or an understanding of common career trajectories for scholars. Moreover, recent research has demonstrated a variety of techniques designed to overcome methodological challenges such as large volumes of data and high dimensionality. However, the effective identification and analysis of latent stages of progression, which can allow for variation within different but similarly evolving event sequences, remain a significant challenge with important real-world motivations. In this paper, we propose an unsupervised stage analysis algorithm to identify semantically meaningful progression stages as well as the critical events which help define those stages. The algorithm follows three key steps: (1) event representation estimation, (2) event sequence warping and alignment, and (3) sequence segmentation. We also present a novel visualization system, ET2, which interactively illustrates the results of the stage analysis algorithm to help reveal evolution patterns across stages. Finally, we report three forms of evaluation for ET2: (1) case studies with two real-world datasets, (2) interviews with domain expert users, and (3) a performance evaluation on the progression analysis algorithm and the visualization design.

show abstract

“…Dang et al [DMAF15] and Wang and Mueller [WM16] proposed tools for the interactive visual analysis of causalities. Dang et al focused on understanding causalities within complex biological pathways and implemented facilities for filtering, clustering, and selecting pathway components.…”

Section: Related Workmentioning

confidence: 99%

Visual Verification of Cancer Staging for Therapy Decision Support

Cypko

Wojdziak

Stöhr

et al. 2017

Computer Graphics Forum

View full text Add to dashboard Cite

It is generally accepted practice that each cancer patient case should be discussed in a clinical expert meeting, the so‐called tumor board. A central role in finding the best therapy options for patients with solid tumors plays the Tumor, lymph Node, and Metastasis staging (TNM staging). Correctness of TNM staging has a significant impact on the therapy choice and hence on the patient's post‐therapeutic quality of life or even survival. If inconsistencies in the TNM staging occur, possible explanations and solutions must be found based on the complex patient records, which takes the costly time of (multiple) physicians. We propose a more efficient visual analysis component, which supports a physician in verifying the given TNM staging before forwarding it to the tumor board. Our component comprises a Bayesian network model of the TNM staging process. Using information from the patient records and Bayesian inference, the models computes a patient‐specific TNM staging, which is then explored and compared to the given staging by means of a graph‐based visualization. Our component is implemented in a research prototype that supports an understanding of the model computations, allows for a fast identification of important influencing factors, and facilitates a quick detection of differences between two TNM stagings. We evaluated our component with five physicians, each studying 20 cases of laryngeal cancer.

show abstract

ReactionFlow: an interactive visualization tool for causality analysis in biological pathways

Cited by 39 publications

References 28 publications

CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components

CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components

Visual Progression Analysis of Event Sequence Data

Visual Verification of Cancer Staging for Therapy Decision Support

Contact Info

Product

Resources

About