Abstract
In recent years, the ability of feature extraction of deep learning models has increased. This ability is used to extract high-level features from minimum pre-processing. We have used Convolutional Neural Networks (CNN), Convolutional Neural Networks Long Short-Term Memory (CNN-LSTM), and Convolutional Neural Networks-Bidirectional Long Short-Term Memory (CNN-Bi-LSTM) for performing the classification of DNA sequences. While doing the classification, we considered the sequences as text data. To represent sequences as input, we used one-hot vectors. The needed position data of nucleotides is preserved by doing this. This project has used the DNA-binding protein sequence dataset, and various metrics are used to evaluate the models. The conclusion we came to after overviewing the results is that CNN-Bidirectional LSTM showed high accuracy of 99.5%
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Siva Jyothi Natha Reddy, B., Yadav, S., Venkatakrishnan, R., Oviya, I.R. (2023). Comparison of Deep Learning Approaches for DNA-Binding Protein Classification Using CNN and Hybrid Models. In: Tripathi, A.K., Anand, D., Nagar, A.K. (eds) Proceedings of World Conference on Artificial Intelligence: Advances and Applications. WWCA 1997. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-5881-8_7
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