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.
Introduction: Membrane proteins play an important role in living organisms as one of the main components of biological membranes. The problem in membrane protein classification and prediction is an important topic of membrane proteomics research because the function of proteins can be quickly determined if membrane protein types can be discriminated. Methods: Most current methods to classify membrane proteins are labor-intensive and require a lot of resources. In this study, five methods, Average Block (AvBlock), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Histogram of Orientation Gradient (HOG), and Pseudo-PSSM (PsePSSM), were used to extract features in order to predict membrane proteins on a large scale. Then, we combined the five obtained feature matrices and constructed the corresponding hypergraph association matrix. Finally, the feature matrices and hypergraph association matrices were integrated to identify the types of membrane proteins using a hypergraph neural network model (HGNN). Results: The proposed method was tested on four membrane protein benchmark datasets to evaluate its performance. The results showed 92.8%, 88.6%, 88.2%, and 99.0% accuracy on each of the four datasets. method: Average block (AvBlock), discrete cosine transform (DCT), discrete wavelet transform (DWT), histogram of orientation gradients (HOG) and pseudo-PSSM (PsePSSM) are used to extract evolutionary features, next, we propose a hypergraph neural network model (HGNN) for integrating five features to identify membrane protein types Conclusion: Compared to traditional machine learning classifier methods, such as Random Forest (RF), Support Vector Machine (SVM), etc., HGNN prediction performance was found to be better. result: For performance evaluation, the proposed method was tested on four membrane protein benchmark Datasets. On the four Datasets, the method of this paper obtained 92.8%, 88.6%, 88.2%, and 99.0
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