ArticlePeerJ. Computer science2021
Application and evaluation of knowledge graph embeddings in biomedical data.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Benchmarking the impact of data leakage on the performance of knowledge graph embedding models for biomedical link prediction.Bioinformatics (Oxford, England) · 2026Article
- Therapeutic monoclonal antibodies repurposing in oncology via IMGT/mAb-KG embeddings.BMC medical informatics and decision making · 2026Article
- KG-DFI: A Prediction of Drug-Food Interactions Based on Knowledge Graph Embedding.Computational and structural biotechnology journal · 2026Article
- The Data Distillery: A Graph Framework for Semantic Integration and Querying of Biomedical Data.bioRxiv : the preprint server for biology · 2025Article
- Deciphering shared molecular dysregulation across Parkinson's disease variants using a multi-modal network-based data integration and analysis.NPJ Parkinson's disease · 2025Article
- Visualization Methods for DNA Sequences: A Review and Prospects.Biomolecules · 2024Review
- BioBLP: a modular framework for learning on multimodal biomedical knowledge graphs.Journal of biomedical semantics · 2023Article
- Article
- Biomedical named entity recognition based on fusion multi-features embedding.Technology and health care : official journal of the European Society for Engineering and Medicine · 2023Article
- OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features.Frontiers in genetics · 2023Article
- LM-DTI: a tool of predicting drug-target interactions using the node2vec and network path score methods.Frontiers in genetics · 2023Article
- Bidirectional matching and aggregation network for few-shot relation extraction.PeerJ. Computer science · 2023Article
- Development of a Knowledge Graph Embeddings Model for Pain.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- A knowledge graph representation learning approach to predict novel kinase-substrate interactions.Molecular omics · 2022Article
- Contexts and contradictions: a roadmap for computational drug repurposing with knowledge inference.Briefings in bioinformatics · 2022Review
- Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning.Scientific reports · 2022Article
- Article
- DTi2Vec: Drug-target interaction prediction using network embedding and ensemble learning.Journal of cheminformatics · 2021Article
- AI-Based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey.IEEE transactions on computational biology and bioinformaticsReview
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Linked data and bio-ontologies enabling knowledge representation, standardization, and dissemination are an integral part of developing biological and biomedical databases. That is, linked data and bio-ontologies are employed in databases to maintain data integrity, data organization, and to empower search capabilities. However, linked data and bio-ontologies are more recently being used to represent information as multi-relational heterogeneous graphs, "knowledge graphs". The reason being, entities and relations in the knowledge graph can be represented as embedding vectors in semantic space, and these embedding vectors have been used to predict relationships between entities. Such knowledge graph embedding methods provide a practical approach to data analytics and increase chances of building machine learning models with high prediction accuracy that can enhance decision support systems. Here, we present a comparative assessment and a standard benchmark for knowledge graph-based representation learning methods focused on the link prediction task for biological relations. We systematically investigated and compared state-of-the-art embedding methods based on the design settings used for training and evaluation. We further tested various strategies aimed at controlling the amount of information related to each relation in the knowledge graph and its effects on the final performance. We also assessed the quality of the knowledge graph features through clustering and visualization and employed several evaluation metrics to examine their uses and differences. Based on this systematic comparison and assessments, we identify and discuss the limitations of knowledge graph-based representation learning methods and suggest some guidelines for the development of more improved methods.
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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.