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机器学习和多学科融合:推进心血管转化研究和临床实践
Mingzhi Lin1, Jiuqi Guo1, Zhilin Gu1
1Department of Cardiology, The First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, People's Republic of China.
人工智能 (AI) 和多omics数据集成正在彻底改变心血管疾病研究. 这些方法为疾病机制和临床应用提供了新的见解.
科学领域:
- 生物医学研究的研究.
- 心血管医学 心血管医学
- 计算生物学是一种计算生物学.
背景情况:
- 心血管疾病 (CVD) 是一个日益严重的全球健康挑战.
- 多组学方法为心血管疾病复杂的生理和病理变化提供了分子洞察力.
- 管理和解释大规模的OMIC数据是生物医学研究中的一个重大障碍.
研究的目的:
- 审查整合人工智能 (AI) 的方法,特别是机器学习,与心血管研究中的omics数据.
- 总结利用多omics数据来探索心血管疾病机制和临床实践的AI模型.
- 突出AI在提取分子信息中的作用,以解决心血管疾病的知识差距.
主要方法:
- 对应用到omics数据的AI和机器学习方法的审查.
- 分析心血管疾病研究中的代表性AI模型.
- 讨论多主题数据集的数据整合策略.
主要成果:
- 与多omics数据集成的AI在心血管研究中显示出前景.
- 人工智能有效地提取潜在的分子信息,解决当前的知识差距.
- 人工智能有助于从潜在机制到临床实践探索心血管疾病.
结论:
- 人工智能和omics集成为推进心血管疾病研究提供了强大的工具.
- 将这些综合方法转化为常规临床实践存在挑战和机遇.
- 未来的AI模型预计将在心血管医学中得到更广泛的应用.
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