Method and system for constructing models for predicting protein-RNA interaction binding sites

Abstract

The invention provides a method and a system for constructing a model for predicting protein-RNA interaction binding sites. Correspondingly, the invention also provides a method and a system for predicting the protein-RNA interaction binding sites by using the method. A deep learning model is trained using sequence characteristics at and upstream and downstream of an RNA and protein binding site and measured RNA structure characteristics, and the model is used to predict a protein-RNA interaction binding site. In the feature extraction process, a motif acquisition module constructed based on aconvolutional neural network and a context semantic acquisition module constructed based on a recurrent neural network are used. Compared with the prior art, the trained model has remarkable progressin the aspects of judgment accuracy, calculation time and universality of an application platform.

Description

technical field [0001] The present invention relates to RNA-protein interaction prediction technology, in particular to a method and system for constructing a model for predicting protein-RNA interaction binding sites, and corresponding methods for predicting protein-RNA interaction binding sites using the method methods and systems. Background technique [0002] At present, the methods for predicting RNA-protein interactions based on deep learning technology mainly include DeepBind (see Alipanahi, B et al., (2015). Nature Biotechnology 33, 831– [0003] 838.), Deepnet-rbp (see Zhang, S et al., (2016). Nucleic Acids Res 44, e32– [0004] e32) and iDeepE (see Pan, X et al., (2018). Bioinformatics 34, 3427–3436), etc. [0005] In the prior art DeepBind method, the model structure uses a convolutional neural network and is trained based on RNA sequence data. Due to the earlier time and the simple structure of the model, the modeling ability is insufficient and the accuracy r...

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Problems solved by technology

Although its accuracy rate is relatively improved, it greatly increases the complexity of the training model, which easily leads to overfitting, and also greatly increases the calculation time for the model to be trained on the computer.

[0008] In addition, the RNA structure used by the Deepnet-rbp method is the predicted in vitro structure, not from the real in vivo data in the experiment, so it is not enough to capture the real RNA structure information under in vivo conditions, so the prediction accuracy of the model is also affected accordingly

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