ArticleBMC medical informatics and decision making2020
KGHC: a knowledge graph for hepatocellular carcinoma.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- RNA-KG v2.0: an RNA-centered Knowledge Graph with Properties.NAR genomics and bioinformatics · 2026Article
- A comprehensive large scale biomedical knowledge graph for AI powered data driven biomedical research.bioRxiv : the preprint server for biology · 2025Article
- MKG-GC: A multi-task learning-based knowledge graph construction framework with personalized application to gastric cancer.Computational and structural biotechnology journal · 2024Article
- Article
- Construction of a knowledge graph for breast cancer diagnosis based on Chinese electronic medical records: development and usability study.BMC medical informatics and decision making · 2023Article
- From Answers to Insights: Unveiling the Strengths and Limitations of ChatGPT and Biomedical Knowledge Graphs.Research square · 2023Article
- From Answers to Insights: Unveiling the Strengths and Limitations of ChatGPT and Biomedical Knowledge Graphs.medRxiv : the preprint server for health sciences · 2023Article
- Article
- AIMedGraph: a comprehensive multi-relational knowledge graph for precision medicine.Database : the journal of biological databases and curation · 2023Article
- Building a knowledge graph to enable precision medicine.Scientific data · 2023Article
- Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities.Journal of big data · 2023Article
- KGML-xDTD: a knowledge graph-based machine learning framework for drug treatment prediction and mechanism description.GigaScience · 2022Article
- Ontologies and Knowledge Graphs in Oncology Research.Cancers · 2022Review
- Head and Tail Entity Fusion Model in Medical Knowledge Graph Construction: Case Study for Pituitary Adenoma.JMIR medical informatics · 2021Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHepatocellular carcinoma is one of the most general malignant neoplasms in adults with high mortality. Mining relative medical knowledge from rapidly growing text data and integrating it with other existing biomedical resources will provide support to the research on the hepatocellular carcinoma. To this purpose, we constructed a knowledge graph for Hepatocellular Carcinoma (KGHC).
methodsWe propose an approach to build a knowledge graph for hepatocellular carcinoma. Specifically, we first extracted knowledge from structured data and unstructured data. Since the extracted entities may contain some noise, we applied a biomedical information extraction system, named BioIE, to filter the data in KGHC. Then we introduced a fusion method which is used to fuse the extracted data. Finally, we stored the data into the Neo4j which can help researchers analyze the network of hepatocellular carcinoma.
resultsKGHC contains 13,296 triples and provides the knowledge of hepatocellular carcinoma for healthcare professionals, making them free of digging into a large amount of biomedical literatures. This could hopefully improve the efficiency of researches on the hepatocellular carcinoma. KGHC is accessible free for academic research purpose at http://202.118.75.18:18895/browser/ .
conclusionsIn this paper, we present a knowledge graph associated with hepatocellular carcinoma, which is constructed with vast amounts of structured and unstructured data. The evaluation results show that the data in KGHC is of high quality.
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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.