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Updated: Jul 2, 2025
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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
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Knowledge Graph for Breast Cancer Prevention and Treatment: Literature-Based Data Analysis Study
Shuyan Jin1, Haobin Liang2, Wenxia Zhang1
1Health Department, Shenzhen Maternity and Child Healthcare Hospital, Shenzhen, China.
JMIR Medical Informatics
|February 27, 2024
Summary
This study developed a knowledge graph to integrate breast cancer prevention and treatment research, revealing key therapies and drugs. The graph aids researchers in discovering complex relationships for improved clinical decision-making.
Area of Science:
- Oncology
- Bioinformatics
- Data Science
Background:
- Rising breast cancer incidence necessitates advanced research integration.
- Existing literature on breast cancer prevention and treatment lacks systematic organization.
- Knowledge graphs offer a structured approach to synthesize complex breast cancer data.
Purpose of the Study:
- To integrate breast cancer treatment and prevention literature data.
- To construct a comprehensive knowledge graph.
- To support clinical decision-making through data integration.
Main Methods:
- Searched PubMed for clinical trial literature (2018-2022) using Medical Subject Headings.
- Extracted triplet data from Semantic MEDLINE Database (SemMedDB).
- Visualized data using NetworkX for knowledge discovery.
Main Results:
- Malignant neoplasms were the most frequent topic (42.3%).
- Pharmacotherapy (19.3%) was the primary treatment, with trastuzumab (11.6%) as a key drug.
- The knowledge graph revealed intricate relationships between treatments, drugs, and preventive measures.
Conclusions:
- A knowledge graph was successfully constructed for breast cancer prevention and treatment.
- The graph integrates and facilitates knowledge discovery from recent literature.
- Researchers can gain insights into treatments, drugs, and preventive strategies.
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