ArticleBioinformatics (Oxford, England)2021
Utilizing image and caption information for biomedical document classification.
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.
What it found
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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.
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.
Who cites it
9 citing papers in PubMed.
- A neural-symbolic AI agent system for biomedical concept mapping.NPJ digital medicine · 2026Article
- Integrating curation into scientific publishing to train AI models.Bioinformatics (Oxford, England) · 2026Article
- RLSuccSite: succinylation sites prediction based on reinforcement learning dynamic with balanced reward mechanism and three-peaks enhanced method for physicochemical property scores.Journal of cheminformatics · 2025Article
- Transformer-Based Language Models for Group Randomized Trial Classification in Biomedical Literature: Model Development and Validation.JMIR medical informatics · 2025Article
- Characterization and automated classification of sentences in the biomedical literature: a case study for biocuration of gene expression and protein kinase activity.Database : the journal of biological databases and curation · 2025Article
- MetaTron: advancing biomedical annotation empowering relation annotation and collaboration.BMC bioinformatics · 2024Article
- An automatic system for extracting figure-caption pair from medical documents: a six-fold approach.PeerJ. Computer science · 2023Article
- Enhancing biomedical search interfaces with images.Bioinformatics advances · 2023Article
- Hagit Shatkay-Reshef 1965-2022.Bioinformatics advances · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
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.
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What OpenQuestion holds
Registered trials
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.