基于 LSTM-CNN 特征融合的 DNA 结合蛋白识别方法研究。
Research on DNA-Binding Protein Identification Method Based on LSTM-CNN Feature Fusion.
机构信息
School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.
Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, China.
出版信息
Comput Math Methods Med. 2022 Jun 2;2022:9705275. doi: 10.1155/2022/9705275. eCollection 2022.
Protein is closely related to life activities. As a kind of protein, DNA-binding protein plays an irreplaceable role in life activities. Therefore, it is very important to study DNA-binding protein, which is a subject worthy of study. Although traditional biotechnology has high precision, its cost and efficiency are increasingly unable to meet the needs of modern society. Machine learning methods can make up for the deficiencies of biological experimental techniques to a certain extent, but they are not as simple and fast as deep learning for data processing. In this paper, a deep learning framework based on parallel long and short-term memory(LSTM) and convolutional neural networks(CNN) was proposed to identify DNA-binding protein. This model can not only further extract the information and features of protein sequences, but also the features of evolutionary information. Finally, the two features are combined for training and testing. On the PDB2272 dataset, compared with PDBP_Fusion model, Accuracy(ACC) and Matthew's Correlation Coefficient (MCC) increased by 3.82% and 7.98% respectively. The experimental results of this model have certain advantages.
蛋白质与生命活动密切相关。作为蛋白质的一种，DNA 结合蛋白在生命活动中发挥着不可替代的作用。因此，研究 DNA 结合蛋白非常重要，这是一个值得研究的课题。虽然传统生物技术具有高精度，但成本和效率越来越不能满足现代社会的需求。机器学习方法在一定程度上可以弥补生物实验技术的不足，但在数据处理方面，它们不如深度学习简单快捷。本文提出了一种基于并行长短时记忆网络（LSTM）和卷积神经网络（CNN）的深度学习框架，用于识别 DNA 结合蛋白。该模型不仅可以进一步提取蛋白质序列的信息和特征，还可以提取进化信息的特征。最后，将这两个特征结合起来进行训练和测试。在 PDB2272 数据集上，与 PDBP_Fusion 模型相比，该模型的准确率（ACC）和马修斯相关系数（MCC）分别提高了 3.82%和 7.98%。该模型的实验结果具有一定的优势。
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