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APDCA:一种准确和有效的方法,用于预测在上皮-介质细胞过渡期间RBPs和AS事件之间的关联

Yangsong He¹, Zheng-Jian Bai², Wai-Ki Ching³

¹School of Mathematical Sciences, Shenzhen University, Shenzhen, People's Republic of China.

PLoS computational biology

|November 6, 2025

概括

我们开发了一种新的计算方法来预测RNA结合蛋白 (RBP) 和替代拼接 (AS) 相互作用,这对癌症转移至关重要. 我们的方法通过整合多样化的数据和使用稀疏性约束来提高准确性,以便更好地发现RBP-AS关联.

科学领域:

计算生物学是一种计算生物学.
生物信息学是一种生物信息学.
癌症研究癌症研究

背景情况:

表皮-介质细胞过渡 (EMT) 通过改变细胞粘附和运动性驱动癌症转移.
RNA结合蛋白 (RBPs) 在EMT期间调节替代拼接 (AS),影响瘤进展.
预测RBP-AS关联对于了解癌症至关重要,但目前的方法在稀疏的数据上扎.

研究的目的:

开发一种改进的计算方法,用于预测RNA结合蛋白 (RBP) 和替代拼接 (AS) 关联.
解决现有模型的局限性,特别是在稀疏的数据设置中.
为了提高预测准确性,利用异构的生物数据和稀疏性约束.

主要方法:

提出了一种使用加速近接直流算法 (APDCA) 的新方法.
APDCA 结合了稀疏的低级矩阵因子化与差异凸度 (DC) 优化框架.
整合了异构的生物数据,并应用了稀疏性约束来推断RBP-AS关联.

主要成果:

与六种基线方法相比,APDCA表现优越,AUC和AUPR更高.
在特定条件下,该算法被证明是趋同的.
一个案例研究验证了RBP QKI的顶级预测与OncoSplicing数据库对比.

结论:

APDCA提供了一个快速,可解释和可扩展的工具,用于识别转录后监管相互作用.
该方法有效地过噪声,并突出了关键的RBP-AS关联.
这种方法提高了对RBP-AS在癌症等疾病中的作用的理解.

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