肥胖生物标志物:探索因素,分支,机器学习和人工智能揭示健康研究中的见解
Ankita Awari1, Deepika Kaushik2, Ashwani Kumar3
1Department of Food Technology and Nutrition Lovely Professional University Phagwara Punjab India.
MedComm
|June 24, 2025
概括
机器学习 (ML) 可以分析复杂的生物数据,以发现和验证肥胖生物标志物. 这种方法有助于了解肥胖症,评估风险,并个性化治疗以获得更好的健康结果.
科学领域:
- 生物医学研究的研究.
- 数据科学是数据科学.
- 基因组学和多基因组学
背景情况:
- 肥胖是一个复杂的全球健康问题,需要精确的诊断和治疗策略.
- 先进的生物科学产生了庞大的数据集,但生物标志物的发现和验证仍然具有挑战性.
- 社会经济因素增加了肥胖管理的复杂性.
研究的目的:
- 探索机器学习 (ML) 在识别和验证肥胖生物标志物的应用.
- 审查先进技术和ML如何增强肥胖研究.
- 以指导未来的数据驱动的肥胖管理战略.
主要方法:
- 关于ML在肥胖研究中的应用现有文献的综述.
- 分析ML如何处理多omics和高通量数据的分析.
- 检查ML在整合像成像和传感器等先进技术数据中的作用.
主要成果:
- 机器学习技术显示出在肥胖研究中处理复杂数据集的巨大潜力.
- ML可以增强新型肥胖生物标志物的发现和验证.
- 机器学习与先进技术的整合为肥胖机制和风险因素提供了可操作的见解.
结论:
- 机器学习是推动肥胖生物标志物发现和验证的强大工具.
- ML促进了数据驱动的方法来理解肥胖症的复杂性.
- 本综述综合了创新,以指导有针对性的,个性化的肥胖管理策略.
关键词:
生物标志物生物标志物数据挖掘是数据挖掘的一个方法.数据库中的知识发现 (KDD)肥胖 肥胖 肥胖 肥胖 肥胖 肥胖 肥胖 肥胖欧米克生物标志物是生物标志物.氧化应激生物标志物氧化应激生物标志物更多相关视频
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