Evidence map›Paper›PMID 30991966›Full record

ArticleBMC bioinformatics2019

Metabopolis: scalable network layout for biological pathway diagrams in urban map style.

Hsiang-Yun Wu, Martin Nöllenburg, Filipa L Sousa, Ivan Viola

Abstract read
In one paragraph

Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. WayFindR: investigating feedback in biological pathways.NAR genomics and bioinformatics · 2026
    Article
  2. Review
  3. Article
  4. Software tools, databases and resources in metabolomics: updates from 2018 to 2019.Metabolomics : Official journal of the Metabolomic Society · 2020
    Review
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Hsiang-Yun WuResearch Division of Computer Graphics, Institute of Visual Computing and Human- Centered Technology, TU Wien, Vienna, Austria. hsiang.yun.wu@acm.org.ORCID http://orcid.org/0000-0003-1028-0010
Martin NöllenburgAlgorithms and Complexity Group, Institute of Logic and Computation, TU Wien, Vienna, Austria.
Filipa L SousaArchaea Biology and Ecogenomics Division, Department of Ecogenomics and Systems Biology, University of Vienna, Vienna, Austria.
Ivan ViolaResearch Division of Computer Graphics, Institute of Visual Computing and Human- Centered Technology, TU Wien, Vienna, Austria.

Funding

Austrian Science Fund P31119European Union Horizon 2020 MSCA 747985King Abdullah University of Science and Technology BAS/1/1680-01-01Vienna Science and Technology Fund VRG11-010Vienna Science and Technology Fund VRG15-007
6 · The paper itself

Abstract

backgroundBiological pathways represent chains of molecular interactions in biological systems that jointly form complex dynamic networks. The network structure changes from the significance of biological experiments and layout algorithms often sacrifice low-level details to maintain high-level information, which complicates the entire image to large biochemical systems such as human metabolic pathways.

resultsOur work is inspired by concepts from urban planning since we create a visual hierarchy of biological pathways, which is analogous to city blocks and grid-like road networks in an urban area. We automatize the manual drawing process of biologists by first partitioning the map domain into multiple sub-blocks, and then building the corresponding pathways by routing edges schematically, to maintain the global and local context simultaneously. Our system incorporates constrained floor-planning and network-flow algorithms to optimize the layout of sub-blocks and to distribute the edge density along the map domain. We have developed the approach in close collaboration with domain experts and present their feedback on the pathway diagrams based on selected use cases.

conclusionsWe present a new approach for computing biological pathway maps that untangles visual clutter by decomposing large networks into semantic sub-networks and bundling long edges to create space for presenting relationships systematically.

Indexed as

Metabolic Networks and PathwaysModels, BiologicalAlgorithmsComputational BiologyHumansMaps as TopicBiological pathwaysEdge routingFloor planningGraph drawingMap metaphorOrthogonal layout

Identifiers

PMID30991966
PMCPMC6466808

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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.