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The biomedical knowledge graph of symptom phenotype in coronary artery plaque: machine learning-based analysis of
Jia-Ming Huan1, Xiao-Jie Wang1, Yuan Li1
1First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China.
Insights
This study introduces a biomedical knowledge graph to link coronary artery plaque symptoms and molecular mechanisms. The model integrates clinical and molecular data, achieving 92.5% AUC for plaque property prediction.
Area of Science:
- Biomedical informatics
- Artificial intelligence in medicine
- Cardiovascular research
Background:
- Coronary artery plaque is a major cause of cardiovascular events, presenting diagnostic challenges due to nonspecific symptoms.
- Integrating diverse data sources is crucial for understanding complex disease mechanisms.
- Knowledge graphs offer a powerful framework for data integration and visualization.
Purpose of the Study:
- To develop a biomedical knowledge graph for visualizing the relationship between coronary artery plaque properties, molecular mechanisms, and clinical symptoms.
- To identify key molecular players and pathways involved in plaque development and symptom presentation.
- To assess the diagnostic potential of identified molecular features.
Main Methods:
- Extracted patient symptomatology from electronic health records.
- Constructed phenotypic networks using clinical and protein-protein interaction data.
- Applied machine learning techniques (CNNs, Dijkstra's algorithm, gene ontology) for feature quantification.
- Trained a K-nearest neighbor model for prediction.
- Utilized weighted correlation network analysis and pathway enrichment analysis.
- Performed coexpression analysis and immune cell infiltration analysis for validation.
Main Results:
- Developed a knowledge graph model integrating clinical and molecular data.
- Identified 23 symptoms, 41 association rules, and 61 hub genes related to plaque properties.
- Achieved an area under the curve (AUC) of 92.5% in predicting plaque characteristics.
- Discovered lipid status-related genes and inflammation-associated pathways contributing to plaque heterogeneity.
- Validated the diagnostic value of hub genes and the reliability of identified pathways.
Conclusions:
- The biomedical knowledge graph effectively integrates clinical and molecular data to elucidate disease mechanisms.
- The model provides insights into the molecular underpinnings of coronary artery plaque symptoms and properties.
- This approach holds potential for improving cardiovascular disease diagnosis and understanding.
Abstract:
A knowledge graph can effectively showcase the essential characteristics of data and is increasingly emerging as a significant means of integrating information in the field of artificial intelligence. Coronary artery plaque represents a significant etiology of cardiovascular events, posing a diagnostic challenge for clinicians who are confronted with a multitude of nonspecific symptoms. To visualize the hierarchical relationship network graph of the molecular mechanisms underlying plaque properties and symptom phenotypes, patient symptomatology was extracted from electronic health record data from real-world clinical settings. Phenotypic networks were constructed utilizing clinical data and protein‒protein interaction networks. Machine learning techniques, including convolutional neural networks, Dijkstra's algorithm, and gene ontology semantic similarity, were employed to quantify clinical and biological features within the network. The resulting features were then utilized to train a K-nearest neighbor model, yielding 23 symptoms, 41 association rules, and 61 hub genes across the three types of plaques studied, achieving an area under the curve of 92.5%. Weighted correlation network analysis and pathway enrichment were subsequently utilized to identify lipid status-related genes and inflammation-associated pathways that could help explain the differences in plaque properties. To confirm the validity of the network graph model, we conducted coexpression analysis of the hub genes to evaluate their potential diagnostic value. Additionally, we investigated immune cell infiltration, examined the correlations between hub genes and immune cells, and validated the reliability of the identified biological pathways. By integrating clinical data and molecular network information, this biomedical knowledge graph model effectively elucidated the potential molecular mechanisms that collude symptoms, diseases, and molecules.
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