Lu Weizhong, Shen Jiawei, Zhang Yu, Wu Hongjie, Qian Yuqing, Chen Xiaoyi, Fu Qiming
School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.
Suzhou Key Laboratory of Virtual Reality Intelligent Interaction and Application Technology, Suzhou University of Science and Technology, Suzhou, China.
Front Genet. 2022 Mar 14;12:834488. doi: 10.3389/fgene.2021.834488. eCollection 2021.
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.
膜蛋白是人体维持正常生命活动能力的重要组成部分。对膜蛋白的进一步研究有助于推动细胞和药物的发展，膜蛋白存在于生命科学研究的各个方面。目前预测蛋白质的方法通常基于机器学习，但预测有效性和准确性仍需进一步提高。在本文中，我们提出了一种基于终身学习的动态深度网络架构，以便更有效地利用计算机对膜蛋白进行分类。该模型扩展了终身学习的应用领域，为生物信息学中的多分类问题提供了新思路。为了验证我们模型的性能，我们在两个数据集上进行了实验，并与其他分类方法进行了比较。结果表明，我们的模型在基准数据集上达到了较高的准确率（95.3%和93.5%），并且与其他方法相比更有效。
