肺部疾病的主要病理生理变化提供了基于深度学习方法的分子洞察力
Swaraj Mohanty1, Poornima Sharma1, Yasmin Ahmad2
1Disruptive & Deterrence Technology (DDT) Division, Defence Institute of Physiology & Allied Sciences (DIPAS), Defence R & D Organization (DRDO), Timarpur, New Delhi, 110054, India.
Scientific reports
|August 31, 2025
概括
肺部疾病的延迟诊断阻碍了有效的治疗. 这项研究使用深度机器学习来确定改善呼吸道疾病诊断和治疗的关键分子途径和生物标志物.
科学领域:
- 肺部医学
- 生物信息学
- 人工智能
背景情况:
- 肺部疾病带来重大健康挑战, 诊断和治疗延迟往往会加剧.
- 由于对病理生理学和分子学的理解不充分, 传统方法难以应对复杂的呼吸道疾病.
- 现有研究缺乏关于分子通路和特征分子的全面数据,限制了氧化 (NO) 和硫化 (H2S) 等治疗方案.
研究的目的:
- 利用深度机器学习和网络分析来阐明主要肺部疾病的分子途径.
- 确定呼吸道疾病进展中的关键分子参与者及其相互联系.
- 提供针对已确定途径的新型治疗干预措施的基础.
主要方法:
- 使用人工智能作为搜索引擎分析科学研究和数据库的深度机器学习方法.
- 关于主要肺部疾病的已发表研究.
- 用于基因和蛋白质信息的NIH数据库.
- 用于路径和分子分析的ShinyGo工具.
- 使用STRING网络对基因表达特征和蛋白质相互作用进行生物信息学分析.
主要成果:
- 确定与肺部疾病相关的关键分子通路.
- 在呼吸道疾病进展中扰乱分子之间的相互作用的演示.
- 网络分析显示了与疾病病理相关的显著分子相互联系.
结论:
- 在呼吸道疾病的进展中起着至关重要的作用.
- 针对已识别的分子通路为肺部疾病提供了潜在的治疗策略.
- 这种人工智能驱动的方法可以提高对分子机制的理解,从而改善疾病管理.
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