ArticlePloS one2016
Knowledge Discovery from Biomedical Ontologies in Cross Domains.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Self-management interventions for chronic kidney disease: a systematic review and meta-analysis.BMC nephrology · 2019Pooled it
- Drug-disease association prediction with literature based multi-feature fusion.Frontiers in pharmacology · 2023Article
- Constructing co-occurrence network embeddings to assist association extraction for COVID-19 and other coronavirus infectious diseases.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Detecting Lifestyle Risk Factors for Chronic Kidney Disease With Comorbidities: Association Rule Mining Analysis of Web-Based Survey Data.Journal of medical Internet research · 2019Article
- HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology.Journal of biomedical informatics · 2019Article
- Rare disease knowledge enrichment through a data-driven approach.BMC medical informatics and decision making · 2019Article
- A comparison of word embeddings for the biomedical natural language processing.Journal of biomedical informatics · 2018Article
- Utilization of Electronic Medical Records and Biomedical Literature to Support the Diagnosis of Rare Diseases Using Data Fusion and Collaborative Filtering Approaches.JMIR medical informatics · 2018Article
- Complex overlapping concepts: An effective auditing methodology for families of similarly structured BioPortal ontologies.Journal of biomedical informatics · 2018Article
- Incorporating Knowledge-Driven Insights into a Collaborative Filtering Model to Facilitate the Differential Diagnosis of Rare Diseases.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Taxonomy-Based Approaches to Quality Assurance of Ontologies.Journal of healthcare engineering · 2017Article
- Predicate Oriented Pattern Analysis for Biomedical Knowledge Discovery.Intelligent information management · 2016Article
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Authors and funding
2 authors.
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Abstract
In recent years, there is an increasing demand for sharing and integration of medical data in biomedical research. In order to improve a health care system, it is required to support the integration of data by facilitating semantic interoperability systems and practices. Semantic interoperability is difficult to achieve in these systems as the conceptual models underlying datasets are not fully exploited. In this paper, we propose a semantic framework, called Medical Knowledge Discovery and Data Mining (MedKDD), that aims to build a topic hierarchy and serve the semantic interoperability between different ontologies. For the purpose, we fully focus on the discovery of semantic patterns about the association of relations in the heterogeneous information network representing different types of objects and relationships in multiple biological ontologies and the creation of a topic hierarchy through the analysis of the discovered patterns. These patterns are used to cluster heterogeneous information networks into a set of smaller topic graphs in a hierarchical manner and then to conduct cross domain knowledge discovery from the multiple biological ontologies. Thus, patterns made a greater contribution in the knowledge discovery across multiple ontologies. We have demonstrated the cross domain knowledge discovery in the MedKDD framework using a case study with 9 primary biological ontologies from Bio2RDF and compared it with the cross domain query processing approach, namely SLAP. We have confirmed the effectiveness of the MedKDD framework in knowledge discovery from multiple medical ontologies.
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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.