克莱德斯 - - 对比学习增强了与正义正对对合的不同接
bioRxiv : the preprint server for biology
|February 27, 2026
概括
我们开发了一种新的对比学习方法,使用进化保护来预测替代拼接变化. 这种方法有效地将序列映射到拼接结果,在各种生物环境中提高准确性.
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:
- 替代拼接 (AS) 产生了多样化的转录和蛋白质,但由于数据有限和实验变异性,预测特定上下文拼接仍然具有挑战性.
- 了解控制AS的基于序列的调节机制对于破译细胞复杂性至关重要.
研究的目的:
- 开发一种强大的方法来学习使用进化保护的序列到拼接映射.
- 改进在各种生物环境中对差异拼接事件 (Δψ) 的预测.
主要方法:
- 提出了一个对比的表示学习框架,利用进化正义序列作为正对.
- 利用一个歧视性的目标来调整监管等效的嵌入,并强调保存的信号.
- 微调了一个用于预测 Δψ 的监督头部,并开发了一个可解释的拼接动机意识分类框架.
主要成果:
- 对比的预训练显著提高了对外因子纳入变化的预测 (Δψ).
- 在对各种基准进行拼接转移 (AUPRC/AUROC) 和竞争回归结果 (RMSE,Spearman) 的分类方面取得了强的表现.
- 通过拼接动机意识证明了学习表征的生物可解释性.
结论:
- 以进化为指导的对比学习为上下文解决的拼接预测提供了一种有效且基于生物学原则的方法.
- 开发的方法增强了对替代拼接中的基于序列的调节的理解.
- 这个框架提供了一个强大的工具来分析跨组织和细胞类型的差异拼接.
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