Identifying Membrane Protein Types Based on Lifelong Learning With Dynamically Scalable Networks

Weizhong Lu; Jiawei Shen; Yu Zhang; Hongjie Wu; Yuqing Qian; Xiaoyi Chen; Qiming Fu

doi:10.3389/fgene.2021.834488

Identifying Membrane Protein Types Based on Lifelong Learning With Dynamically Scalable Networks

Front Genet. 2022 Mar 14:12:834488. doi: 10.3389/fgene.2021.834488. eCollection 2021.

Authors

Weizhong Lu^{1

2

3}, Jiawei Shen¹, Yu Zhang⁴, Hongjie Wu^{1

2}, Yuqing Qian¹, Xiaoyi Chen¹, Qiming Fu¹

Affiliations

¹ School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.
² Suzhou Key Laboratory of Virtual Reality Intelligent Interaction and Application Technology, Suzhou University of Science and Technology, Suzhou, China.
³ Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, China.
⁴ Suzhou Industrial Park Institute of Services Outsourcing, Suzhou, China.

Abstract

Membrane proteins are an essential part of the body's ability to maintain normal life activities. Further research into membrane proteins, which are present in all aspects of life science research, will help to advance the development of cells and drugs. The current methods for predicting proteins are usually based on machine learning, but further improvements in prediction effectiveness and accuracy are needed. In this paper, we propose a dynamic deep network architecture based on lifelong learning in order to use computers to classify membrane proteins more effectively. The model extends the application area of lifelong learning and provides new ideas for multiple classification problems in bioinformatics. To demonstrate the performance of our model, we conducted experiments on top of two datasets and compared them with other classification methods. The results show that our model achieves high accuracy (95.3 and 93.5%) on benchmark datasets and is more effective compared to other methods.

Keywords: dynamically scalable networks; evolutionary features; lifelong learning; membrane proteins; position specific scoring matrix.