Enrichment Analysis and Deep Learning in Biomedical Ontology: Applications and Advancements.
- Author:
Hong-Yu FU
1
;
Yang-Yang LIU
1
;
Mei-Yi ZHANG
1
;
Hai-Xiu YANG
2
Author Information
- Publication Type:Review
- Keywords: biomedical ontology; deep learning; enrichment analysis; ontology annotation; ontology hierarchy
- MeSH: Deep Learning; Biological Ontologies; Humans; Big Data; Biomedical Research
- From: Chinese Medical Sciences Journal 2025;40(1):45-56
- CountryChina
- Language:English
- Abstract: Biomedical big data, characterized by its massive scale, multi-dimensionality, and heterogeneity, offers novel perspectives for disease research, elucidates biological principles, and simultaneously prompts changes in related research methodologies. Biomedical ontology, as a shared formal conceptual system, not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research. In this review, we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties, highlighting how technological advancements are enabling the more comprehensive use of ontology information. Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list. Deep learning, on the other hand, represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction. With the continuous evolution of big data technologies, the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.
