DNA-binding protein prediction based on deep transfer learning

Mathematical Biosciences and Engineering

Hongjie Wu¹  Tengsheng Jiang²  Shixuan Guan²  Haiou Li²  Jun Yan²  Yaoyao Lu²  Junkai Liu²  Yijie Ding³ 

[1] 1. College of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China 2. Suzhou Smart City Research Institute, Suzhou University of Science and Technology, Suzhou, China;1. College of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China;3. Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China;

关键词: dna-binding protein;    deep learning;    transfer learning;   

DOI  :  10.3934/mbe.2022362

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown