Evidence map›Paper›PMID 37270143›Full record

ArticleJournal of biomedical informatics2023

Creating an ignorance-base: Exploring known unknowns in the scientific literature.

Mayla R Boguslav, Nourah M Salem, Elizabeth K White, Katherine J Sullivan, Michael Bada, Teri L Hernandez, Sonia M Leach, Lawrence E Hunter

Abstract read
In one paragraph

Article in Journal of biomedical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Semantics-enabled biomedical literature analytics.Journal of biomedical informatics · 2024
    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

8 authors.

Mayla R BoguslavComputational Bioscience Program, University of Colorado, Anschutz Medical Campus, E 17th Avenue, Aurora, 80045, CO, USA. Electronic address: Mayla.Boguslav@CUAnschutz.edu.
Nourah M SalemComputational Bioscience Program, University of Colorado, Anschutz Medical Campus, E 17th Avenue, Aurora, 80045, CO, USA.
Elizabeth K WhiteComputational Bioscience Program, University of Colorado, Anschutz Medical Campus, E 17th Avenue, Aurora, 80045, CO, USA; Center for Genes, Environment and Health, National Jewish Health, Jackson Street, Denver, 80206, CO, USA.
Katherine J SullivanComputational Bioscience Program, University of Colorado, Anschutz Medical Campus, E 17th Avenue, Aurora, 80045, CO, USA.
Michael BadaComputational Bioscience Program, University of Colorado, Anschutz Medical Campus, E 17th Avenue, Aurora, 80045, CO, USA.
Teri L HernandezCollege of Nursing, Department of Medicine/Division of Endocrinology, Metabolism, & Diabetes, University of Colorado, Anschutz Medical Campus, E 17th Avenue, Aurora, 80045, CO, USA.
Sonia M LeachComputational Bioscience Program, University of Colorado, Anschutz Medical Campus, E 17th Avenue, Aurora, 80045, CO, USA; Center for Genes, Environment and Health, National Jewish Health, Jackson Street, Denver, 80206, CO, USA.
Lawrence E HunterComputational Bioscience Program, University of Colorado, Anschutz Medical Campus, E 17th Avenue, Aurora, 80045, CO, USA.

Funding

Technology Development for a MolBio Knowledge-BaseR01LM008111 · NLM · UNIVERSITY OF COLORADO DENVER · PI HUNTER, LAWRENCE E · 2004 to 2021
$9.7M
Scientific Questions: A New Target for Biomedical NLPR01LM013400 · NLM · UNIVERSITY OF COLORADO DENVER · PI BAUMGARTNER, WILLIAM ANTHONY · 2020 to 2023
$1.8M
NLM NIH HHS R01 LM008111NLM NIH HHS R01 LM013400
6 · The paper itself

Abstract

backgroundScientific discovery progresses by exploring new and uncharted territory. More specifically, it advances by a process of transforming unknown unknowns first into known unknowns, and then into knowns. Over the last few decades, researchers have developed many knowledge bases to capture and connect the knowns, which has enabled topic exploration and contextualization of experimental results. But recognizing the unknowns is also critical for finding the most pertinent questions and their answers. Prior work on known unknowns has sought to understand them, annotate them, and automate their identification. However, no knowledge-bases yet exist to capture these unknowns, and little work has focused on how scientists might use them to trace a given topic or experimental result in search of open questions and new avenues for exploration. We show here that a knowledge base of unknowns can be connected to ontologically grounded biomedical knowledge to accelerate research in the field of prenatal nutrition.

resultsWe present the first ignorance-base, a knowledge-base created by combining classifiers to recognize ignorance statements (statements of missing or incomplete knowledge that imply a goal for knowledge) and biomedical concepts over the prenatal nutrition literature. This knowledge-base places biomedical concepts mentioned in the literature in context with the ignorance statements authors have made about them. Using our system, researchers interested in the topic of vitamin D and prenatal health were able to uncover three new avenues for exploration (immune system, respiratory system, and brain development) by searching for concepts enriched in ignorance statements. These were buried among the many standard enriched concepts. Additionally, we used the ignorance-base to enrich concepts connected to a gene list associated with vitamin D and spontaneous preterm birth and found an emerging topic of study (brain development) in an implied field (neuroscience). The researchers could look to the field of neuroscience for potential answers to the ignorance statements.

conclusionOur goal is to help students, researchers, funders, and publishers better understand the state of our collective scientific ignorance (known unknowns) in order to help accelerate research through the continued illumination of and focus on the known unknowns and their respective goals for scientific knowledge.

Indexed as

KnowledgeKnowledge BasesNatural Language ProcessingFemaleHumansInfant, NewbornPremature BirthPublicationsVitamin DVitamin DEpistemologyInformation extractionKnowledge-baseKnowledge representationNatural language processing

Identifiers

PMID37270143
PMCPMC10528083

What OpenQuestion holds

Textmetadata
LicenceTDM
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.