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KGHC: a knowledge graph for hepatocellular carcinoma.

Nan Li1, Zhihao Yang2, Ling Luo1

  • 1College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China.

BMC Medical Informatics and Decision Making
|July 11, 2020
PubMed
Summary
This summary is machine-generated.

Researchers developed a knowledge graph for Hepatocellular Carcinoma (KGHC) by extracting and filtering data from various sources. This KGHC resource aids healthcare professionals in hepatocellular carcinoma research.

Keywords:
Hepatocellular carcinomaInformation extractionKnowledge graph

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Area of Science:

  • Biomedical Informatics
  • Oncology
  • Data Science

Background:

  • Hepatocellular carcinoma (HCC) is a prevalent malignancy with high mortality.
  • Effective mining of medical knowledge from extensive text data is crucial for HCC research.
  • Integrating diverse biomedical information supports advancements in understanding HCC.

Purpose of the Study:

  • To construct a comprehensive knowledge graph for Hepatocellular Carcinoma (KGHC).
  • To facilitate easier access to and analysis of HCC-related medical knowledge.
  • To support researchers and healthcare professionals in their work on hepatocellular carcinoma.

Main Methods:

  • Knowledge extraction from both structured and unstructured data sources.
  • Application of the BioIE biomedical information extraction system for data filtering.
  • Implementation of a data fusion method and storage in Neo4j for network analysis.

Main Results:

  • The constructed KGHC contains 13,296 triples.
  • Provides readily accessible knowledge on hepatocellular carcinoma, reducing literature review time.
  • KGHC is available for academic research at http://202.118.75.18:18895/browser/.

Conclusions:

  • A novel knowledge graph (KGHC) for hepatocellular carcinoma has been developed.
  • The graph integrates vast amounts of structured and unstructured data.
  • Evaluation confirms the high quality of the data within KGHC.