Evidence map›Paper›PMID 31557157›Full record

ArticlePLoS computational biology2019

Ten simple rules to create biological network figures for communication.

G Elisabeta Marai, Bruno Pinaud, Katja Bühler, Alexander Lex, John H Morris

Abstract readEditorial
In one paragraph

Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy.IEEE transactions on visualization and computer graphics · 2022
    Article
  6. Review
  7. Article
  8. Article
  9. Precision association of lymphatic disease spread with radiation-associated toxicity in oropharyngeal squamous carcinomas.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2021
    Article
  10. Article
  11. Article
  12. Molecular networks in Network Medicine: Development and applications.Wiley interdisciplinary reviews. Systems biology and medicine · 2020
    Review
  13. Explainable Spatial Clustering: Leveraging Spatial Data in Radiation Oncology.IEEE Visualization Conference : VIS. IEEE Conference on Visualization · 2020
    Article
  14. Article
  15. 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

5 authors.

G Elisabeta MaraiElectronic Visualization Laboratory, University of Illinois at Chicago, Chicago, Illinois, United States of America.ORCID 0000-0002-7212-9669
Bruno PinaudLaboratoire Bordelais de Recherche en Informatique, University of Bordeaux, Bordeaux, France.ORCID 0000-0003-4814-3273
Katja BühlerBiomedical Image Informatics Department, VRVis Research Center, Vienna, Austria.
Alexander LexDepartment of Computer Science, University of Utah, Salt Lake City, Utah, United States of America.ORCID 0000-0001-6930-5468
John H MorrisDepartment of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, United States of America.ORCID 0000-0003-0290-7979

Funding

TR&D 3 - Network Guided Machine LearningP41GM103504 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI IDEKER, TREY · 2012 to 2024
$17.3M
Uncovering Clinical Evidence in COVID-19 Publications: An Integrated Search via Text & ImagesR01LM012527 · NLM · UNIVERSITY OF DELAWARE · PI EIGENMANN, RUDOLF, MARAI, GEORGETA-ELISABETA · 2017 to 2020
$1.9M
SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer TreatmentR01CA214825 · NCI · UNIVERSITY OF ILLINOIS AT CHICAGO · PI CANAHUATE, GUADALUPE, FULLER, CLIFTON DAVID · 2017 to 2019
$1.1M
NCI NIH HHS R01 CA214825NIGMS NIH HHS P41 GM103504NLM NIH HHS R01 LM012527
6 · The paper itself

Abstract

Biological network figures are ubiquitous in the biology and medical literature. On the one hand, a good network figure can quickly provide information about the nature and degree of interactions between items and enable inferences about the reason for those interactions. On the other hand, good network figures are difficult to create. In this paper, we outline 10 simple rules for creating biological network figures for communication, from choosing layouts, to applying color or other channels to show attributes, to the use of layering and separation. These rules are accompanied by illustrative examples. We also provide a concise set of references and additional resources for each rule.

Indexed as

Computer GraphicsAttentionColorComputational BiologyHumansProtein Interaction MapsSignal TransductionVisual Perception

Identifiers

PMID31557157
PMCPMC6762067

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.