Diff-SE:一种扩散增强的对比学习框架,用于超级增强器预测
Haolu Zhou1, Yu Han1, Yude Bai2
1School of Artificial Intelligence, Hebei University of Technology, Tianjin 300400, China.
Journal of chemical information and modeling
|July 4, 2025
概括
Diff-SE是一个新的深度学习框架,通过使用数据增强和对比学习的扩散模型来改善超级增强器 (SE) 预测. 这种方法提高了SE识别的准确性和跨物种概括性.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 分子生物学分子生物学
背景情况:
- 超级增强剂 (SE) 是控制基因表达的关键 cis 调控元素.
- SE与癌症和阿尔茨海默氏症等疾病有关.
- 目前的识别方法 (ChIP-seq) 是资源密集型的,计算方法在数据不平衡和概括方面扎.
研究的目的:
- 开发一个先进的计算框架,用于准确和强大的超级增强器预测.
- 克服现有方法的局限性,包括类不平衡和跨物种表现差.
主要方法:
- 提出了 Diff-SE,这是一个深度学习框架,集成了基于扩散的数据增强和对比学习.
- 扩散模块生成合成SE数据以平衡训练集.
- 对比式学习增强了特征表示,改善了歧视.
主要成果:
- 与基线模型相比,Diff-SE在八个数据集的精度,MCC和F1得分中实现了10%至30%的改进.
- 在跨物种验证 (人类和老鼠) 中表现出卓越的概括能力.
结论:
- Diff-SE在计算超级增强器预测方面取得了重大进展.
- 该框架为传统方法提供了更准确,更普遍和更有效的替代方案.
- 现有的代码和数据有助于进一步研究SE和相关疾病.
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