Evidence map›Paper›PMID 34252939›Full record

ArticleBioinformatics (Oxford, England)2021

Utilizing image and caption information for biomedical document classification.

Pengyuan Li, Xiangying Jiang, Gongbo Zhang, Juan Trelles Trabucco, Daniela Raciti, Cynthia Smith, Martin Ringwald, G Elisabeta Marai, Cecilia Arighi, Hagit Shatkay

Erratum issuedAbstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
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  9. Hagit Shatkay-Reshef 1965-2022.Bioinformatics advances · 2022
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Pengyuan LiDepartment of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA.
Xiangying JiangDepartment of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA.
Gongbo ZhangDepartment of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA.
Juan Trelles TrabuccoDepartment of Computer Science, The University of Illinois at Chicago, Chicago, IL 60612, USA.
Daniela RacitiDivision of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
Cynthia SmithThe Jackson Laboratory, Bar Harbor, ME 04609, USA.
Martin RingwaldThe Jackson Laboratory, Bar Harbor, ME 04609, USA.
G Elisabeta MaraiDepartment of Computer Science, The University of Illinois at Chicago, Chicago, IL 60612, USA.
Cecilia ArighiDepartment of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA.
Hagit ShatkayDepartment of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA.

Funding

Technical DevelopmentP41HD062499 · NICHD · JACKSON LABORATORY · PI MARTIN RINGWALD · 2011 to 2026
$37.3M
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
National Institute of Child Health and Human DevelopmentNICHD NIH HHS P41 HD062499NIH HHSNLM NIH HHS R01 LM012527NLM NIH HHS R56LM011354A
6 · The paper itself

Abstract

motivationBiomedical research findings are typically disseminated through publications. To simplify access to domain-specific knowledge while supporting the research community, several biomedical databases devote significant effort to manual curation of the literature-a labor intensive process. The first step toward biocuration requires identifying articles relevant to the specific area on which the database focuses. Thus, automatically identifying publications relevant to a specific topic within a large volume of publications is an important task toward expediting the biocuration process and, in turn, biomedical research. Current methods focus on textual contents, typically extracted from the title-and-abstract. Notably, images and captions are often used in publications to convey pivotal evidence about processes, experiments and results.

resultsWe present a new document classification scheme, using both image and caption information, in addition to titles-and-abstracts. To use the image information, we introduce a new image representation, namely Figure-word, based on class labels of subfigures. We use word embeddings for representing captions and titles-and-abstracts. To utilize all three types of information, we introduce two information integration methods. The first combines Figure-words and textual features obtained from captions and titles-and-abstracts into a single larger vector for document representation; the second employs a meta-classification scheme. Our experiments and results demonstrate the usefulness of the newly proposed Figure-words for representing images. Moreover, the results showcase the value of Figure-words, captions and titles-and-abstracts in providing complementary information for document classification; these three sources of information when combined, lead to an overall improved classification performance. AVAILABILITY AND IMPLEMENTATION: Source code and the list of PMIDs of the publications in our datasets are available upon request.

Indexed as

Biomedical ResearchDatabases, Factual

Identifiers

PMID34252939
PMCPMC8346654

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.