Open AccessDNA-binding protein prediction based on deep transfer learning
Author(s) -
Jun Yan,
Tengsheng Jiang,
Junkai Liu,
Yaoyao Lu,
Shixuan Guan,
Haiou Li,
Hongjie Wu,
Yongsheng Ding
Publication year - 2022
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2022362
Subject(s) - transfer of learning , construct (python library) , artificial intelligence , computer science , deep learning , field (mathematics) , diction , artificial neural network , machine learning , sequence (biology) , biological system , chemistry , biology , mathematics , biochemistry , art , poetry , literature , pure mathematics , programming language
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.