Identification of post-translational modification-related biomarkers in ischemic stroke using bioinformatics and machine learning
8.5
来源：
Nature
关键字：
mRNA
发布时间：
2025-11-11 03:48
摘要：
This study identifies six post-translational modification-related biomarkers for ischemic stroke (IS) using bioinformatics and machine learning techniques. Key genes ATG7, KAT2A, RNF20, UBA1, UBE2I, and USP15 were validated as diagnostic markers with high accuracy (AUC > 0.7). The research highlights the potential of these biomarkers in improving IS diagnosis and treatment strategies, emphasizing the role of post-translational modifications in the disease's pathophysiology.
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domain_focus
1.0
business_impact
0.5
scientific_rigor
1.5
timeliness_innovation
1.5
investment_perspective
2.5
market_value_relevance
1.0
team_institution_background
0.5
technical_barrier_competition
0.5
关键证据
Six genes identified as diagnostic markers with AUC > 0.7.
ANN model showed high diagnostic accuracy (AUC = 0.983 in training, 0.95 in testing).
Study provides new potential therapeutic biomarkers and molecular pathways for ischemic stroke.
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AI评分总结
This study identifies six post-translational modification-related biomarkers for ischemic stroke (IS) using bioinformatics and machine learning techniques. Key genes ATG7, KAT2A, RNF20, UBA1, UBE2I, and USP15 were validated as diagnostic markers with high accuracy (AUC > 0.7). The research highlights the potential of these biomarkers in improving IS diagnosis and treatment strategies, emphasizing the role of post-translational modifications in the disease's pathophysiology.