首页|Shandong University of Traditional Chinese Medicine Reports Findings in Machine Learning (The biomedical knowledge graph of symptom phenotype in coronary artery plaque: machine learning-based analysis of real-world clinical data)
Shandong University of Traditional Chinese Medicine Reports Findings in Machine Learning (The biomedical knowledge graph of symptom phenotype in coronary artery plaque: machine learning-based analysis of real-world clinical data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating in Jinan, People’ s Republic of China, by NewsRx journalists, research stated, “A knowledge graph can effectively showcase the essential characteristics of data and is increasing ly emerging as a significant means of integrating information in the field of ar tificial intelligence. Coronary artery plaque represents a significant etiology of cardiovascular events, posing a diagnostic challenge for clinicians who are c onfronted with a multitude of nonspecific symptoms.” The news reporters obtained a quote from the research from the Shandong Universi ty of Traditional Chinese Medicine, “To visualize the hierarchical relationship network graph of the molecular mechanisms underlying plaque properties and sympt om phenotypes, patient symptomatology was extracted from electronic health recor d data from real-world clinical settings. Phenotypic networks were constructed u tilizing clinical data and protein-protein interaction networks. Machine learnin g techniques, including convolutional neural networks, Dijkstra’s algorithm, and gene ontology semantic similarity, were employed to quantify clinical and biolo gical features within the network. The resulting features were then utilized to train a K-nearest neighbor model, yielding 23 symptoms, 41 association rules, an d 61 hub genes across the three types of plaques studied, achieving an area unde r the curve of 92.5%. Weighted correlation network analysis and pat hway enrichment were subsequently utilized to identify lipid status-related gene s and inflammation-associated pathways that could help explain the differences i n plaque properties. To confirm the validity of the network graph model, we cond ucted coexpression analysis of the hub genes to evaluate their potential diagnos tic value. Additionally, we investigated immune cell infiltration, examined the correlations between hub genes and immune cells, and validated the reliability o f the identified biological pathways.”
JinanPeople’s Republic of ChinaAsiaAngiologyCardiologyCoronary ArteryCyborgsEmerging TechnologiesGenetic sHealth and MedicineMachine Learning
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