MOPSOGAT:通过改进的多目标粒子群集优化和图形注意力网络预测circRNA疾病关联
1School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China.
Interdisciplinary sciences, computational life sciences
|June 13, 2025
概括
我们开发了MOPSOGAT,这是一种使用多目标粒子群优化和图形注意网络来预测circRNA疾病关联的新方法. 这种方法优化了模型参数,以提高疾病生物标志物发现的准确性.
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:
- 循环RNAs (circRNAs) 越来越被认为是各种疾病的关键生物标志物.
- 深度学习模型被广泛用于circRNA疾病关联预测,但参数优化仍然具有挑战性.
- 在circRNA-disease网络中的异质图形结构可能导致偏向的特征学习.
研究的目的:
- 引入MOPSOGAT,一种用于预测circRNA疾病关联的新方法.
- 为了优化图的注意力网络参数,使用多目标粒子群优化算法.
- 解决因异质图中节点分布不平衡而导致的特征学习限制.
主要方法:
- 使用circRNA和疾病相似性数据构建异质图.
- 使用随机步行与跳跃和停留策略来获得节点序列.
- 使用word2vec进行初始节点嵌入生成.
- 应用一个改进的多目标粒子群集优化 (MOPSO) 进行参数优化.
- 通过图形注意力网络完善嵌入.
主要成果:
- 与现有的深度学习,多目标优化和机器学习方法相比,MOPSOGAT表现出卓越的性能.
- 该方法有效地优化图表注意力网络的参数,确保模型的峰值效率.
- 案例研究验证了预测的circRNA与疾病的关联,突出了MOPSOGAT的潜力.
- 这种方法减轻了异质图中的特征学习偏差.
结论:
- MOPSOGAT提供了一种强大而高效的方法来预测circRNA与疾病的关联.
- 整合MOPSO和图表注意力网络显著提高了预测准确度.
- 这种方法有望促进生物医学研究和疾病生物标志物识别.
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