ArticleBMC bioinformatics2018
SemaTyP: a knowledge graph based literature mining method for drug discovery.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- A graph retrieval-augmented generation pipeline for systematic drug target discovery: validation and application to ocular neovascularization.Briefings in bioinformatics · 2026Article
- Inference of dependency knowledge graph for Electronic Health Records.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2026Article
- Virtual Screening of Phytochemicals From Medicinal Plants as Promising PDE5 Inhibitors Against Erectile Dysfunction.Food science & nutrition · 2026Article
- Predicting implicit concept embeddings for singular relationship discovery replication of closed literature-based discovery.Frontiers in research metrics and analytics · 2025Article
- Context-Aware Contrastive Representation Learning for Zero-Shot Biomedical Text Classification.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2024Article
- Visualization Methods for DNA Sequences: A Review and Prospects.Biomolecules · 2024Review
- Mining literature and pathway data to explore the relations of ketamine with neurotransmitters and gut microbiota using a knowledge-graph.Bioinformatics (Oxford, England) · 2024Article
- Phytochemical profiling andFrontiers in chemistry · 2024Article
- Comparing methods for drug-gene interaction prediction on the biomedical literature knowledge graph: performance versus explainability.BMC bioinformatics · 2023Article
- Research on named entity recognition method of marine natural products based on attention mechanism.Frontiers in chemistry · 2023Article
- Exploring relationship between emotion and probiotics with knowledge graphs.Health information science and systems · 2022Article
- Towards a knowledge graph for pre-/probiotics and microbiota-gut-brain axis diseases.Scientific reports · 2022Article
- An automatic hypothesis generation for plausible linkage between xanthium and diabetes.Scientific reports · 2022Article
- AnthraxKP: a knowledge graph-based, Anthrax Knowledge Portal mined from biomedical literature.Database : the journal of biological databases and curation · 2022Article
- Optimizations for Computing Relatedness in Biomedical Heterogeneous Information Networks: SemNet 2.0.Big data and cognitive computing · 2022Article
- Knowledge-based approaches to drug discovery for rare diseases.Drug discovery today · 2022Review
- Challenges and opportunities in network-based solutions for biological questions.Briefings in bioinformatics · 2022Article
- Article
- Knowledge Graphs for Indication Expansion: An Explainable Target-Disease Prediction Method.Frontiers in genetics · 2022Article
- Mining a stroke knowledge graph from literature.BMC bioinformatics · 2021Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDrug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries.
methodsHere, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies' existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases.
resultsThe experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs.
conclusionsIn this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery 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.