使用ICD-11代码和负采样进行无偏的microRNA-Disease关联预测.
Munyoung Chang1,2,3, Jeonghee Jo4, Junyong Ahn5,6
1Education and Research Program for Future ICT Pioneers, Seoul National University, Seoul, South Korea.
Pharmacology research & perspectives
|November 28, 2025
概括
我们开发了无偏向的microRNA疾病关联预测器 (UBMDA) 来预测microRNA疾病联系. UBMDA使用疾病代码和核酸序列,使得新的微RNA和疾病的分析.
科学领域:
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
- 计算生物学 计算生物学
背景情况:
- 微RNA与疾病的关联对于理解疾病机制和开发向疗法至关重要.
- 现有的计算模型通常依赖于基于相似性的方法,限制了它们对新型或未经研究的微RNA和疾病的适用性.
- 缺乏全面的负样本数据集阻碍了准确的预测模型开发.
研究的目的:
- 开发一种新的计算模型,即无偏微RNA疾病关联预测器 (UBMDA),用于预测微RNA疾病关联.
- 通过利用国际疾病分类 (ICD-11) 代码和microRNA核酸序列作为输入特征来克服以前方法的局限性.
- 构建一个平衡的负样本数据集,以解释微RNA和疾病频率的潜在偏差.
主要方法:
- 开发了UBMDA,一个使用ICD-11疾病代码和microRNA核酸序列用于特征提取的计算模型.
- 通过仔细考虑积极样本数据集中存在的microRNA和疾病的频率来创建负样本数据集,以防止预测偏差.
- 实施了一项战略,以确保积极和消极样本数据集之间的微RNA和疾病频率相似.
主要成果:
- 成功开发了具有简单和直观结构的UBMDA计算模型.
- 证明了模型在不依赖基于相似性的特征提取的情况下预测microRNA疾病关联的能力.
- 该方法解决了微RNA研究中有限的负样本数据的挑战.
结论:
- UBMDA提供了一种新的方法来预测微RNA与疾病的关联,适用于新发现或缺乏特征的微RNA和疾病.
- 该模型的设计,利用ICD-11代码和核酸序列,增强了其多功能性和预测能力.
- 预计UBMDA将加速微RNA相关生物标志物和治疗策略的发现.
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