DNA methylation prediction method and system based on BERT framework
A prediction method and methylation technology, applied in the field of biological information, can solve the problems of relying on prior knowledge and difficult to be generally applicable, and achieve the effect of performance improvement
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Embodiment 1
[0036] This embodiment provides a DNA methylation prediction method based on the BERT framework;
[0037] Such as figure 1 As shown, the DNA methylation prediction method based on the BERT framework includes:
[0038] S101: Obtain the DNA sequence to be predicted;
[0039] S102: Input the DNA sequence to be predicted into the trained deep learning model for predicting DNA methylation, obtain the predicted probability of methylation of the DNA sequence to be predicted, and obtain the final methylation prediction result according to the predicted probability ;
[0040] Wherein, the deep learning model for predicting DNA methylation after the training is based on the model architecture in the deep bidirectional Transformer language model pre-trained by Pre-training of Deep Bidirectional Transformers for Language Understanding (BERT), and the cross-entropy loss function and The experience weighted mutual information is combined and applied to the training process of the deep le...
Embodiment 2
[0091] This embodiment provides a DNA methylation prediction system based on the BERT framework;
[0092] A DNA methylation prediction system based on the BERT framework, including:
[0093] An acquisition module configured to: acquire the DNA sequence to be predicted;
[0094] The prediction module is configured to: input the DNA sequence to be predicted into the deep learning model for predicting DNA methylation after training, obtain the predicted probability of methylation of the DNA sequence to be predicted, and obtain the final The methylation prediction results of ;
[0095] Wherein, the trained deep learning model for predicting DNA methylation is based on the BERT model, and is obtained by combining the cross-entropy loss function and empirical weighted mutual information in the deep learning model training process.
[0096] The specific network model structure of the deep learning model for predicting DNA methylation includes: sequentially connected input module, f...
Embodiment 3
[0112] This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, the processor is connected to the memory, and the one or more computer programs are programmed Stored in the memory, when the electronic device is running, the processor executes one or more computer programs stored in the memory, so that the electronic device executes the method described in Embodiment 1 above.
[0113] It should be understood that in this embodiment, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, o...
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