DMGAT:根据扩散图和异质图注意力网络的基础上预测ncRNA-药物耐药性关联
Tingyu Liu1, Qiuhao Chen2, Renjie Liu2
1School of Medicine and Heath, Harbin Institute of Technology, 150000, Nangang District, Xidazhi Street No. 90, Harbin, China.
Briefings in bioinformatics
|April 19, 2025
概括
这项研究介绍了DMGAT,这是一种用于预测非编码RNA (ncRNA) -药物关联的新型深度学习模型. DMGAT有效地捕获序列信息并整合异质数据,优于生物标志物发现的现有方法.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 非编码RNAs (ncRNAs) 在耐药性和敏感性方面至关重要,作为潜在的生物标志物和治疗点.
- 预测ncRNA-药物关联受到数据不平衡,稀疏性和现有模型捕获序列信息的限制的阻碍.
研究的目的:
- 开发一种新的深度学习模型,DMGAT (扩散图和异质图注意力网络),用于准确预测ncRNA-药物关联.
- 为了应对数据集不平衡等挑战,并加强捕获本地和全球序列信息以进行可靠的预测.
主要方法:
- DMGAT集成了用于序列嵌入的扩散图,用于特征提取的图形卷积网络,以及用于信息融合的异质图形注意网络 (GAT).
- 该模型使用word2vec嵌入ncRNA序列和药物SMILES,并使用序列和高斯相互作用配置文件内核相似性构建异质网络.
- 通过结合灵敏度关联和使用随机森林分类器进行负样本选择来解决数据集不平衡.
主要成果:
- 在一个精心策划的数据集上,DMGAT在五倍交叉验证方面取得了卓越的性能,超过了七种最先进的方法.
- 该模型在接收器操作特征曲线 (0.8964) 下的面积最高,在精度回忆曲线 (0.8984),回忆 (0.9576) 和F1得分 (0.8285) 下的面积最高.
结论:
- DMGAT显示出识别ncRNA与药物关联的巨大潜力,为生物标志物发现提供了强大的方法.
- 该模型能够整合多种数据类型并捕获复杂的序列信息,从而提高预测可靠性.
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