DNA-Binding-Proteins Identification Based on Hybrid Features Extraction from Hidden Markov Model
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-03918-8_13
Reference28 articles.
1. Qian, Y., Jiang, L., Ding, Y., Tang, J., Guo, F.: A sequence based multiple Kernel model for identifying DNA binding proteins. BMC Bioinform. 22, 291 (2021)
2. Saber, S., Khairuddin, U., Yusof, R., Madan, A.: DTLM-DBP: deep transfer learning models for DNA binding proteins identification. Comput. Mater. Continua 68(3), 3563–3576 (2021)
3. Li, G., Du, X., Li, X., Zou, L., Zhang, G., Wu, Z.: Prediction of DNA binding proteins using local features and long-term dependencies with primary sequences based on deep learning. PeerJ 9, e11262 (2021)
4. Zhou, J., Lu, Q., Xu, R., Gui, L., Wang, H.: EL_LSTM: prediction of DNA-binding residue from protein sequence by combining long short-term memory and ensemble learning. IEEE/ACM Trans. Comput. Biol. Bioinform. 17(1), 124–135 (2020)
5. Hu, J., Rao, L., Zhu, Y.H., Zhang, G.J., Yu, D.J.: TargetDBP: enhancing the performance of identifying DNABinding proteins via weighted convolutional features. J. Chem. Inf. Model. 61(1), 505–515 (2021)
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