ArticleFrontiers in neuroinformatics2021
Integrative Base Ontology for the Research Analysis of Alzheimer's Disease-Related Mild Cognitive Impairment.
Article in Frontiers in neuroinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Knowledge graph and its application in the study of neurological and mental disorders.Frontiers in psychiatry · 2025Review
- Conventional and Emerging Drug Targeting Sites in Alzheimer's Disease and the Role of Translational Informatics in its Diagnosis and Management.Current neuropharmacology · 2025Review
- Semantics-enabled biomedical literature analytics.Journal of biomedical informatics · 2024Article
- Early detection of mild cognitive impairment through neuropsychological tests in population screenings: a decision support system integrating ontologies and machine learning.Frontiers in neuroinformatics · 2024Article
- Design of the formalized and integrated Alzheimer's Disease Ontology and its application in retrieving textual data via text mining.Database : the journal of biological databases and curation · 2023Article
- Ontological Model in the Identification of Emotional Aspects in Alzheimer Patients.Healthcare (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Early detection of mild cognitive impairment (MCI) has become a priority in Alzheimer's disease (AD) research, as it is a transitional phase between normal aging and dementia. However, information on MCI and AD is scattered across different formats and standards generated by different technologies, making it difficult to work with them manually. Ontologies have emerged as a solution to this problem due to their capacity for homogenization and consensus in the representation and reuse of data. In this context, an ontology that integrates the four main domains of neurodegenerative diseases, diagnostic tests, cognitive functions, and brain areas will be of great use in research. Here, we introduce the first approach to this ontology, the Neurocognitive Integrated Ontology (NIO), which integrates the knowledge regarding neuropsychological tests (NT), AD, cognitive functions, and brain areas. This ontology enables interoperability and facilitates access to data by integrating dispersed knowledge across different disciplines, rendering it useful for other research groups. To ensure the stability and reusability of NIO, the ontology was developed following the ontology-building life cycle, integrating and expanding terms from four different reference ontologies. The usefulness of this ontology was validated through use-case scenarios.
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