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

Journal: Frontiers in genetics
Published Date: Mar 14, 2022

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.

Authors

  • Weizhong Lu
    School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.
  • Jiawei Shen
    School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.
  • Yu Zhang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Hongjie Wu
    School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.
  • Yuqing Qian
    School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.
  • Xiaoyi Chen
    Department of Ultrasound, Shenzhen Children's Hospital of China Medical University, Shenzhen, China.
  • Qiming Fu
    School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.

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