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AI4ACEIP:一种计算工具,通过合并的分子表征和丰富的内在序列信息来识别具有高度抑制ACE活性的食物,基于集体学习策略

Sen Yang^1,2, Jiaqi Ni¹, Piao Xu³

¹School of Computer Science and Artificial Intelligence, Aliyun School of Big Data School of Software, Changzhou University, Changzhou 213164, China.

Journal of agricultural and food chemistry

|November 4, 2024

概括

研究人员开发了AI4ACEIP,这是一种新型模型,用于识别抑制血管酶转化酶 (ACE) 的食物衍生. 这种饮食方法为管理高血压提供了一个有希望的替代方案,其副作用比传统药物少.

科学领域:

生物技术是生物技术.
计算生物学计算生物学
心血管药理学心血管药理学

背景情况:

高血压是一种常见的慢性疾病,也是心血管疾病的重要危险因素.
ангиотензин转化酶 (ACE) 通过将 ангиотензин I 转化为 ангиотензин II 在血压调节中发挥关键作用.
目前用于高血压的药物治疗有局限性,并可能导致不良影响.

研究的目的:

提出和验证一个新的计算模型,AI4ACEIP,用于识别食品来源的有效ACE抑制 (ACEIP).
探索从序列,大型语言模型和ACEIP预测分子数据中集成特征的有效性.
建立一个可靠的工具,以发现饮食中的ACEIP,对高血压有潜在的治疗益处.

主要方法:

开发了AI4ACEIP,这是一个采用集成功能的双层堆叠组合模型.
采用序列,大型语言模型和基于分子的信息来提取特征.
利用PowerShap进行功能选择,以确定最佳的功能和元模型组合.

主要成果:

与现有方法相比,AI4ACEIP模型在识别ACEIP方面表现优异.
在马修的相关系数中观察到显著的改善,在基准数据集上从8.47到20.65%不等.
分析显示,特定的集成特征对增强了预测性能.

结论:

AI4ACEIP是一个强大的,可靠的模型,用于预测ACE抑制.
该模型为发现支持高血压管理的饮食ACEIP提供了有价值的工具.
AI4ACEIP模型及其代码是公开可用于研究的.

关键词:

血管氨酸转化酶 - 血管氨酸转化酶组合学习组合学习功能对的特征对是指特征对.食品衍生的类.

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