Evidence map›Paper›PMID 34224898›Full record

ArticleJournal of biomedical informatics2021

Visual comprehension and orientation into the COVID-19 CIDO ontology.

Ling Zheng, Yehoshua Perl, Yongqun He, Christopher Ochs, James Geller, Hao Liu, Vipina K Keloth

Abstract read
In one paragraph

Article in Journal of biomedical informatics, 2021. 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

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

4 citing papers in PubMed.

  1. Big knowledge visualization of the COVID-19 CIDO ontology evolution.BMC medical informatics and decision making · 2023
    Article
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  3. Article
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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

7 authors.

Ling ZhengComputer Science and Software Engineering Department, Monmouth University, West Long Branch, NJ, USA. Electronic address: lzheng@monmouth.edu.
Yehoshua PerlDepartment of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA.
Yongqun HeDepartment of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
Christopher OchsNokia Bell Labs, Murray Hills, NJ, USA.
James GellerDepartment of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA.
Hao LiuColumbia University Irving Medical Center, New York, NY, USA.
Vipina K KelothDepartment of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA.

Funding

New Jersey Alliance for Clinical Translational Science: NJ ACTSUL1TR003017 · NCATS · RUTGERS BIOMEDICAL/HEALTH SCIENCES-RBHS · PI PANETTIERI, REYNOLD ALEXANDER · 2019 to 2023
$28.9M
NCATS NIH HHS UL1 TR003017
6 · The paper itself

Abstract

The current intensive research on potential remedies and vaccinations for COVID-19 would greatly benefit from an ontology of standardized COVID terms. The Coronavirus Infectious Disease Ontology (CIDO) is the largest among several COVID ontologies, and it keeps growing, but it is still a medium sized ontology. Sophisticated CIDO users, who need more than searching for a specific concept, require orientation and comprehension of CIDO. In previous research, we designed a summarization network called "partial-area taxonomy" to support comprehension of ontologies. The partial-area taxonomy for CIDO is of smaller magnitude than CIDO, but is still too large for comprehension. We present here the "weighted aggregate taxonomy" of CIDO, designed to provide compact views at various granularities of our partial-area taxonomy (and the CIDO ontology). Such a compact view provides a "big picture" of the content of an ontology. In previous work, in the visualization patterns used for partial-area taxonomies, the nodes were arranged in levels according to the numbers of relationships of their concepts. Applying this visualization pattern to CIDO's weighted aggregate taxonomy resulted in an overly long and narrow layout that does not support orientation and comprehension since the names of nodes are barely readable. Thus, we introduce in this paper an innovative visualization of the weighted aggregate taxonomy for better orientation and comprehension of CIDO (and other ontologies). A measure for the efficiency of a layout is introduced and is used to demonstrate the advantage of the new layout over the previous one. With this new visualization, the user can "see the forest for the trees" of the ontology. Benefits of this visualization in highlighting insights into CIDO's content are provided. Generality of the new layout is demonstrated.

Indexed as

Biological OntologiesCommunicable DiseasesCOVID-19ComprehensionHumansSARS-CoV-2Comprehension of an ontologyCoronavirus Infectious Disease OntologyOntology orientationOntology summarizationOntology visualizationSummarization networks

Identifiers

PMID34224898
PMCPMC8252699

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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.