CirRBP

Circular RNA (circRNA) can exert biological functions by interacting with RNA-binding protein (RBP), and some deep learning-based methods have been developed to predict RBP binding sites on circRNA. However, most of these methods identify circRNA-RBP binding sites are only based on single data resource and cannot provide exact binding sites, only providing the probability value of a sequence fragment. To solve these problems, we propose a binding sites localization algorithm that fuses binding sites from multiple databases, and further design a stacked generalization ensemble deep learning model named CirRBP to identify RBP binding sites on circRNA. The CirRBP is trained by combining the binding sites from multiple databases and makes predictions by weighted aggregating the predictions of each sub-model. The results show that the CirRBP outperforms any sub-model and existing online prediction model. For better access to our research results, we develop an open-source web application called CRWS (CircRNA-RBP Web Server).环状RNA（circular RNA, circRNA）可通过与RNA结合蛋白（RNA-binding protein, RBP）相互作用发挥生物学功能，目前已有诸多基于深度学习的方法被开发，用于预测circRNA上的RBP结合位点。然而，此类用于识别circRNA-RBP结合位点的方法大多仅基于单一数据源，且仅能提供序列片段的结合概率值，无法输出精确的结合位点。为解决上述问题，本研究提出一种融合多数据库结合位点的位点定位算法，并进一步构建了名为CirRBP的堆叠泛化集成深度学习模型，用于识别circRNA上的RBP结合位点。CirRBP通过整合多数据库的结合位点数据进行训练，并通过加权聚合各子模型的预测结果完成最终预测。实验结果表明，CirRBP的性能优于所有子模型及现有在线预测模型。为便于科研人员获取本研究成果，我们开发了一款开源网页应用CRWS（CircRNA-RBP Web Server）。

搜集汇总

背景与挑战

背景概述

该数据集名为CirRBP，专注于预测环状RNA（circRNA）上RNA结合蛋白（RBP）的结合位点。它整合了多个数据库的数据，采用深度学习模型进行训练，在37个独立RBP数据集上平均AUC值超过0.9，表现出可靠的预测性能。