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MDFGNN-SMMA:基于多源数据融合和图形神经网络的潜在小分子-miRNA关联的预测
Jianwei Li1, Xukun Zhang1, Bing Li1
1School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, China.
BMC bioinformatics
|January 13, 2025
概括
这项研究引入了一个深度学习模型,MDFGNN-SMMA,用于预测小分子-微RNA关联. 该模型的性能优于现有方法,有助于疾病研究和药物开发.
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
- 药物发现 药物发现 药物发现
背景情况:
- 微RNAs (miRNAs) 在人类疾病中至关重要,并且是小分子 (SM) 药物的目标.
- 鉴定SM-miRNA关联至关重要,但由于昂贵的实验方法,具有挑战性.
研究的目的:
- 开发一种高效的计算方法来预测小分子-miRNA关联.
- 引入小分子-米RNA协会 (MDFGNN-SMMA) 模型的多源数据融合和图形神经网络.
主要方法:
- 使用原子对和MACS指纹提取SM特征;使用K-mer序列生成miRNA特征.
- 构建的邻近矩阵使用共弦相似性SMs和miRNAs.
- 在深度学习框架内使用Graph Attention Network (GAT) 和GraphSAGE来生成预测的最终特征向量.
主要成果:
- 与四个最先进的模型相比,MDFGNN-SMMA在10倍的交叉验证中取得了更高的性能,由高AUC和AUPR证明.
- 一个独立的测试集验证了该模型强大的概括能力.
- 案例研究证实了该模型的有效性,高比例的预测SM-miRNA关联得到了文献和数据库的验证.
结论:
- MDFGNN-SMMA提供了一种有效的计算方法,用于预测小分子-miRNA关联.
- 该模型的准确性和概括能力支持其在药物发现和疾病研究中的实用性.
- 经过验证的预测突出了特定的SM-miRNA相互作用的潜力,以便进一步研究.
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