在不同类型细胞内，以及从被转录到被转运到细胞内其他位置发挥功能的过程中，RNA会与其他不同分子包括RNA结合蛋白（RBP）结合，其结构也会发生动态变化。RBP-RNA相互作用与RNA结构紧密相关。然而由于缺乏细胞内RNA结构信息，我们对这两者之间的相互影响了解不多。.本项目通过机器学习建立包含RNA结构信息的RBP-RNA相互作用模型。首先利用一种我们新建立的、可以在细胞内测得RNA二级结构信息的技术icSHAPE，来获得不同细胞系不同亚细胞组分的RNA结构数据。进一步利用深度学习，结合icSHAPE RNA结构数据和数据库中收集的RBP-RNA结合位点数据，建立RBP-RNA相互作用模型并进行验证。该模型能够预测更多条件下RBP-RNA的结合。特别的，RNA序列的变化可以影响其结构进而影响RBP结合并引发疾病，因而该模型可以用来预测基因变异的患病风险，从而在精准医疗方面得到应用。

In different types of cells and also in the course from being transcribed to transferred to different cellular compartments for its functioning, RNA interacts with many other different molecules including RNA binding proteins (RBPs), a process often coupled with RNA structure changes. As research suggested, RNA structures play very important roles in determining RBP-RNA interactions. However, due to the lack of in vivo RNA structure information, the knowledge of RNA structure dynamics and its interplay with RBP-RNA interactions remains very limited. .The objective of this proposal is to establish a machine-learning model of RBP-RNA interactions that include RNA structure information. We first use icSHAPE, our newly established transcriptome-wide in vivo RNA secondary structure probing technology, to obtain structure information for RNAs of different types of cells and from different subcellular compartments. We then use deep learning algorithms to build a convolutional neural network of RBP-RNA interaction model for further validation, based on our RNA structure probing data and RBP-RNA interaction data retrieved from public available databases of CLIP-seq experiments. Once trained in one cellular condition, this neural network model can predict RBP-RNA interactions in another condition as long as RNA structure information is available. In particular, genomic variations can affect RBP-RNA interaction by changing RNA structures, and finally possibly result in diseases. Thus this model can be used to predict the disease risk of certain mutations and find its applications in the practice of precision medicine.