Biomedical data, computational methods and tools for evaluating disease–disease associations
Abstract
In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.
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Clinical decision-making is inherently multimodal, as diseases manifest through complex interactions among imaging phenotypes, molecular signatures, physiological measurements, and textual clinical documentation.
“However, clinical decision-making is inherently multimodal, as diseases manifest through complex interactions among imaging phenotypes, molecular signatures, physiological measurements, and textual clinical documentation.”
From The role of multimodality in clinical disease diagnosis: advances, challenges, and opportunities (2026) · cites this paper as [8]
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Flagged first, then by citations
Cited by
Sorted by citations
The role of multimodality in clinical disease diagnosis: advances, challenges, and opportunities
Nawaz, A., Edinat, A., Rana, M. R. R., Ali, T., Mustafa, G., Tahir, S., & Lee, S. W. (2026) · Frontiers in public health · doi:10.3389/fpubh.2026.1788454 · Cited by 3 · see citing works
- Authors
- Xiang, J., Zhang, J., Zhao, Y., Wu, F.-X., & Li, M.
- Affiliations
- Central South University (CN)
- University of Saskatchewan (CA)
- Corresponding author
- Li, Min
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-02-07
- Volume
- 23
- Issue
- 2
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- OpenAlex
- W4205464551
- Cited by
- 49
- References
- 291
- Citations by year
- 2026: 25 · 2025: 8 · 2024: 4 · 2023: 9 · 2022: 3
- FWCI
- 0.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Briefings in Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Key Research and Development Program of China (2019YFA0706202)
- National Natural Science Foundation of China (61702054)
- Hunan Provincial Science and Technology Program (2019CB1007, 2021RC0048)
- Training Program for Excellent Young Innovators of Changsha (kq2106075)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- disease relationships, complex diseases, biomedical databases, representation learning, text mining, molecular pathogenesis
- MeSH terms
- Phenotype, Software, Databases, Factual, Computational Biology, 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.
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