Evidence map›Paper›PMID 26361502›Full record

ArticleBMC proceedings2015

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

Tuan Nhon Dang, Paul Murray, Jillian Aurisano, Angus Graeme Forbes

Abstract read
In one paragraph

Article in BMC proceedings, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. The Kappa platform for rule-based modeling.Bioinformatics (Oxford, England) · 2018
    Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Tuan Nhon DangDepartment of Computer Science M/C 152, University of Illinois at Chicago, 851 S. Morgan, Room 1120, Chicago 60607-7053, IL, USA.
Paul MurrayDepartment of Computer Science M/C 152, University of Illinois at Chicago, 851 S. Morgan, Room 1120, Chicago 60607-7053, IL, USA.
Jillian AurisanoDepartment of Computer Science M/C 152, University of Illinois at Chicago, 851 S. Morgan, Room 1120, Chicago 60607-7053, IL, USA.
Angus Graeme ForbesDepartment of Computer Science M/C 152, University of Illinois at Chicago, 851 S. Morgan, Room 1120, Chicago 60607-7053, IL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMolecular and systems biologists are tasked with the comprehension and analysis of incredibly complex networks of biochemical interactions, called pathways, that occur within a cell. Through interviews with domain experts, we identified four common tasks that require an understanding of the causality within pathways, that is, the downstream and upstream relationships between proteins and biochemical reactions, including: visualizing downstream consequences of perturbing a protein; finding the shortest path between two proteins; detecting feedback loops within the pathway; and identifying common downstream elements from two or more proteins.

resultsWe introduce ReactionFlow, a visual analytics application for pathway analysis that emphasizes the structural and causal relationships amongst proteins, complexes, and biochemical reactions within a given pathway. To support the identified causality analysis tasks, user interactions allow an analyst to filter, cluster, and select pathway components across linked views. Animation is used to highlight the flow of activity through a pathway.

conclusionsWe evaluated ReactionFlow by providing our application to two domain experts who have significant experience with biomolecular pathways, after which we conducted a series of in-depth interviews focused on each of the four causality analysis tasks. Their feedback leads us to believe that our techniques could be useful to researchers who must be able to understand and analyze the complex nature of biological pathways. ReactionFlow is available at https://github.com/CreativeCodingLab/ReactionFlow.

Indexed as

Biological networksCausality analysisPathway visualizationTopological ordering

Identifiers

PMID26361502
PMCPMC4547159

What OpenQuestion holds

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.