Biomedical text mining for research rigor and integrity: tasks, challenges, directions
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Abstract
An estimated quarter of a trillion US dollars is invested in the biomedical research enterprise annually. There is growing alarm that a significant portion of this investment is wasted because of problems in reproducibility of research findings and in the rigor and integrity of research conduct and reporting. Recent years have seen a flurry of activities focusing on standardization and guideline development to enhance the reproducibility and rigor of biomedical research. Research activity is primarily communicated via textual artifacts, ranging from grant applications to journal publications. These artifacts can be both the source and the manifestation of practices leading to research waste. For example, an article may describe a poorly designed experiment, or the authors may reach conclusions not supported by the evidence presented. In this article, we pose the question of whether biomedical text mining techniques can assist the stakeholders in the biomedical research enterprise in doing their part toward enhancing research integrity and rigor. In particular, we identify four key areas in which text mining techniques can make a significant contribution: plagiarism/fraud detection, ensuring adherence to reporting guidelines, managing information overload and accurate citation/enhanced bibliometrics. We review the existing methods and tools for specific tasks, if they exist, or discuss relevant research that can provide guidance for future work. With the exponential increase in biomedical research output and the ability of text mining approaches to perform automatic tasks at large scale, we propose that such approaches can support tools that promote responsible research practices, providing significant benefits for the biomedical research enterprise.
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Automated screening based on natural language processing and machine learning could reduce the burden of manual checking, streamline the peer review process, and contribute to better reporting quality.
“Automated screening, based on natural language processing (NLP) and machine learning approaches, could reduce the burden of manual checking, streamline the peer review process, and contribute to better reporting quality18–20.”
From Text classification models for assessing the completeness of randomized controlled trial publications based on CONSORT reporting guidelines (2024) · cites this paper as [18]
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Asadi, N., Badie, K., & Mahmoudi, M. T. (2019) · Scientometrics · doi:10.1007/s11192-019-03060-9 · Cited by 12 · see citing works
Badie, K., Asadi, N., & Mahmoudi, M. T. (2018) · Journal of Information and Telecommunication · doi:10.1080/24751839.2018.1460083 · Cited by 12 · see citing works
MoNERo: a Biomedical Gold Standard Corpus for the Romanian Language
Mitrofan, M., Mititelu, V. B., & Mitrofan, G. (2019) · BioNLP Workshop and Shared Task (BioNLP) · doi:10.18653/v1/w19-5008 · Cited by 11 · see citing works
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Literature-Based Enrichment Insights into Redox Control of Vascular Biology
Essack, M., Salhi, A., Stanimirović, J., Tifratene, F., Raies, A. B., Hungler, A., Uludağ, M., Neste, C. V., Trpković, A., Bajić, V., et al. (2019) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2019/1769437 · Cited by 9 · see citing works
Hassan, N. A., Seoud, R. A. A. A. A., & Salem, D. (2024) · Journal of Advances in Information Technology · doi:10.12720/jait.15.6.723-734 · Cited by 8 · see citing works
Lan, M., Cheng, M., Hoang, L., ter Riet, G., & Halil Kilicoglu, B. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104628 · Cited by 8 · see citing works
Macnee, M., Pérez‐Palma, E., Schumacher, S. M., Dalton, J. E., Leu, C., Blankenberg, D. J., & Lal, D. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab365 · Cited by 8 · see citing works
Identifying Genes in Published Pathway Figure ImagesPreprint
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Bhasuran, B., & Natarajan, J. (2018) · Lecture notes in networks and systems · doi:10.1007/978-981-13-2354-6_39 · Cited by 5 · see citing works
Discovering New Trends & Connections: Current Applications of Biomedical Text Mining
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Knowledge-Based Intelligent Text Simplification for Biological Relation Extraction
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DES-ROD: Exploring Literature to Develop New Links between RNA Oxidation and Human Diseases
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Collaborative Digital Research: Case Study of Text Mining a Corpus of Academic Journals
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BioBERT-based SNP-trait Associations Extraction from Biomedical Literature
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Combining Literature Mining and Machine Learning for Predicting Biomedical Discoveries
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Unsupervised inference of implicit biomedical events using context triggers
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A new approach to zone identification based on considering features with high semantic richness
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Secondary Analysis of Individual Patient-Level Clinical Trial Data: A Primer
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Writing an Effective Medical Manuscript and Avoiding Errors for Smooth Publication
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NLP Applications—Biomedical Literature
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Informatics and literature search
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MatNexus: A Comprehensive Text Mining and Analysis Suite for Materials DiscoveryPreprint
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MatNexus: A Comprehensive Text Mining and Analysis Suite for Materials DiscoverPreprint
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- Authors
- Kilicoglu, H.
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-06-13
- Volume
- 19
- Issue
- 6
- Pages
- 1400-1414
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- PMC
- PMC6291799
- OpenAlex
- W2952174148
- Cited by
- 61
- References
- 188
- Citations by year
- 2025: 2 · 2024: 7 · 2023: 7 · 2022: 6 · 2021: 10 · 2020: 6 · 2019: 9 · 2018: 10 · 2017: 4
- FWCI
- 3.24
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- Locations
- Briefings in Bioinformatics (publishedVersion, public-domain)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- U.S. National Library of Medicine
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Keywords
- biomedical text mining, research integrity, research rigor, reproducibility, research waste, plagiarism detection, reporting guidelines adherence, information overload, citation analysis, bibliometric analysis, responsible conduct of research, grant applications, scholarly publishing
- MeSH terms
- Natural Language Processing, Publications, Reproducibility of Results, Biomedical Research, Data Mining
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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