基于知识图的代谢物疾病关联的识别
Fuheng Xiao1, Canling Huang1, Ali Chen2
1School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, P.R. China.
Metabolomics : Official journal of the Metabolomic Society
|February 22, 2025
概括
这项研究介绍了COM-RAN,一种机器学习模型,使用知识图来预测疾病代谢物相关性,为疾病研究提供了传统实验室实验的更快的替代方案.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 代谢学 代谢学 代谢学
背景情况:
- 传统的用于代谢物分析的湿实验室实验是耗时和劳动密集的.
- 代谢物分析提供了对疾病发病,进展和潜在治疗方法的关键见解.
- 开发有效的方法来识别疾病代谢物协会对于推动医学研究至关重要.
研究的目的:
- 开发一种新的机器学习模型,COM-RAN,用于识别代谢物和疾病之间的潜在关联.
- 通过利用计算方法来克服传统实验方法的局限性.
- 提高疾病代谢物协会预测的准确性和效率.
主要方法:
- 已知疾病代谢因子相关性的整合.
- 综合有关疾病和代谢物的现有数据,并提供补充信息.
- 使用基于知识图的嵌入式特征对疾病代谢物协会的表征.
- 使用随机森林算法构建预测模型.
主要成果:
- 在5倍的交叉验证中,COM-RAN模型实现了0.968的接收器操作特征曲线 (AUC) 下的高面积和0.901的精度回忆曲线 (AUPR) 下的面积.
- 该模型在识别疾病代谢物关联方面超过了大多数现有的预测方法.
- 案例研究验证了COM-RAN预测的大多数新兴关联,证实了其可靠性.
结论:
- 对于预测疾病与代谢物相关性,COM-RAN模型显著有前途.
- 将知识图与机器学习相结合,可以提高预测的准确性和可靠性.
- 这种方法为发现新型疾病相关代谢物和推进个性化医疗提供了强大的工具.
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