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DNA-binding protein prediction based on deep transfer learning


  • Received: 11 March 2022 Revised: 03 May 2022 Accepted: 06 May 2022 Published: 24 May 2022
  • The study of DNA binding proteins (DBPs) is of great importance in the biomedical field and plays a key role in this field. At present, many researchers are working on the prediction and detection of DBPs. Traditional DBP prediction mainly uses machine learning methods. Although these methods can obtain relatively high pre-diction accuracy, they consume large quantities of human effort and material resources. Transfer learning has certain advantages in dealing with such prediction problems. Therefore, in the present study, two features were extracted from a protein sequence, a transfer learning method was used, and two classical transfer learning algorithms were compared to transfer samples and construct data sets. In the final step, DBPs are detected by building a deep learning neural network model in a way that uses attention mechanisms.

    Citation: Jun Yan, Tengsheng Jiang, Junkai Liu, Yaoyao Lu, Shixuan Guan, Haiou Li, Hongjie Wu, Yijie Ding. DNA-binding protein prediction based on deep transfer learning[J]. Mathematical Biosciences and Engineering, 2022, 19(8): 7719-7736. doi: 10.3934/mbe.2022362

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  • The study of DNA binding proteins (DBPs) is of great importance in the biomedical field and plays a key role in this field. At present, many researchers are working on the prediction and detection of DBPs. Traditional DBP prediction mainly uses machine learning methods. Although these methods can obtain relatively high pre-diction accuracy, they consume large quantities of human effort and material resources. Transfer learning has certain advantages in dealing with such prediction problems. Therefore, in the present study, two features were extracted from a protein sequence, a transfer learning method was used, and two classical transfer learning algorithms were compared to transfer samples and construct data sets. In the final step, DBPs are detected by building a deep learning neural network model in a way that uses attention mechanisms.



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