图表基于注意力网络的MicroRNA疾病因果关系的预测,在头部和部瘤
Pradeep Kumar Yadalam1, Swarnambiga Ayyachamy2, Prabhu M Natarajan3
1Department of Periodontics, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, SIMATS, Saveetha University, Chennai, Tamil Nadu, 600077, India.
Scientific reports
|November 17, 2025
概括
图表注意力网络 (GATs) 准确地预测了微RNA (miRNA) 与头癌的关联. 这种深度学习方法识别了参与口腔癌途径的关键miRNA,有助于未来的诊断和治疗.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 头部和部癌症对全球健康造成重大负担.
- 微RNAs (miRNAs) 越来越多地被认为是它们在癌症发展中的作用.
- 了解miRNA与疾病的关联对于向治疗至关重要.
研究的目的:
- 采用图表注意力网络 (GATs) 来预测头癌中的miRNA关联.
- 为了确定新的miRNA贡献者,头部和部瘤.
- 阐明导致口腔癌进展的分子机制.
主要方法:
- 使用HMDD v4.0数据库查找miRNA疾病关联数据.
- 使用双部分图形构建miRNA疾病网络.
- 应用图表注意力网络 (GAT) 具有用于预测分析的注意力机制.
主要成果:
- 该GAT模型在预测头部瘤的miRNA疾病关联方面取得了83%的准确性.
- 功能性丰富分析确定了在口腔癌途径中显著的miRNA参与.
- 突出了TGF-β和PI3K-Akt信号在瘤进展中的作用.
结论:
- 基于图形的深度学习,特别是GATs,显示出对精确的miRNA-疾病关联预测有很大的潜力.
- 这些发现有助于了解头癌的分子基础.
- 有潜力推进头癌的诊断和治疗策略.
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