ArticleBioinformatics (Oxford, England)2017
Neuro-symbolic representation learning on biological knowledge graphs.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
44 citing papers in PubMed, 149 citations in OpenAlex.
- Ontological deskilling: clinical expertise and the changing disease object in precision medicine.Medicine, health care, and philosophy · 2026Article
- Graph-based representations in modern protein science.The Biochemical journal · 2026Review
- A review on knowledge graphs for healthcare: Resources, applications, and promises.Journal of biomedical informatics · 2025Review
- GTPLM-GO: Enhancing Protein Function Prediction Through Dual-Branch Graph Transformer and Protein Language Model Fusing Sequence and Local-Global PPI Information.International journal of molecular sciences · 2025Article
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- TinyNS: Platform-Aware Neurosymbolic Auto Tiny Machine Learning.ACM transactions on embedded computing systems : TECS · 2024Article
- Review
- Patient-centric knowledge graphs: a survey of current methods, challenges, and applications.Frontiers in artificial intelligence · 2024Review
- How will generative AI disrupt data science in drug discovery?Nature biotechnology · 2023Article
- Knowledge Graph Embeddings for ICU readmission prediction.BMC medical informatics and decision making · 2023Article
- Development of a Knowledge Graph Embeddings Model for Pain.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- The case for data science in experimental chemistry: examples and recommendations.Nature reviews. Chemistry · 2022Review
- Current progress and open challenges for applying deep learning across the biosciences.Nature communications · 2022Review
- Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning.Scientific reports · 2022Article
- Evaluating semantic similarity methods for comparison of text-derived phenotype profiles.BMC medical informatics and decision making · 2022Article
- Article
- Representation Learning: Recommendation With Knowledge GraphFrontiers in genetics · 2022Article
- DTi2Vec: Drug-target interaction prediction using network embedding and ensemble learning.Journal of cheminformatics · 2021Article
- Catalyzing Knowledge-Driven Discovery in Environmental Health Sciences through a Community-Driven Harmonized Language.International journal of environmental research and public health · 2021Article
- Semantic similarity and machine learning with ontologies.Briefings in bioinformatics · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationBiological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. Results: We develop a novel method for feature learning on biological knowledge graphs. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs. Through the use of symbolic logic, these embeddings contain both explicit and implicit information. We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target relations, and demonstrate performance that matches and sometimes outperforms traditional approaches based on manually crafted features. Our method can be applied to any biological knowledge graph, and will thereby open up the increasing amount of Semantic Web based knowledge bases in biology to use in machine learning and data analytics. AVAILABILITY AND IMPLEMENTATION: https://github.com/bio-ontology-research-group/walking-rdf-and-owl. CONTACT: robert.hoehndorf@kaust.edu.sa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.