LPItabformer:通过表式变压器增强预测lncRNA-蛋白相互作用的概括性
Qin Lin1,2,3, Jie Sheng1,2,3, Chang Zhou1,2,3
1State Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200120, China.
Computational and structural biotechnology journal
|June 16, 2025
概括
预测长非编码RNA-蛋白相互作用 (LPIs) 对于理解RNA功能至关重要. 我们新的LPItabformer模型通过解决LPI数据集中的数据偏差来提高预测准确性和概括性.
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:
- 长非编码RNAs (LncRNAs) 是生物过程和疾病的关键调节者.
- 准确预测lncRNA-蛋白相互作用 (LPIs) 对于阐明lncRNA功能和致病机制至关重要.
- 现有的计算LPI预测方法通常由于数据泄露和忽视的生物特性而表现出不良的概括性.
研究的目的:
- 开发一个强大的计算框架,用于预测具有增强的概括能力的LPIs.
- 为了解决当前LPI预测方法中观察到的概括缺陷.
- 提高LPI预测的准确性和可靠性.
主要方法:
- 建议LPItabformer,一个专门为LPI预测而设计的表格式变压器框架.
- 整合了一个域变化与不确定性 (DSU) 模块,以增强模型通用化.
- 在数据分割过程中利用蛋白质集群来识别和减轻数据泄露问题.
主要成果:
- LPItabformer有效地减轻了由数据偏差和蛋白质结合模式偏好引起的概括性挑战.
- 该模型在与最先进的方法相比,在人类和小鼠LPI数据集上表现出卓越的稳定性和概括性.
- 成功验证了LPItabformer在预测新型LPIs方面的能力.
结论:
- LPItabformer在计算LPI预测方面取得了重大进展,克服了现有方法的局限性.
- 该框架为研究 lncRNA 功能和疾病机制的研究人员提供了更可靠的工具.
- 开发的模型对发现新的LPIs和推进我们对lncRNA生物学的理解充满希望.
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