DNA-binding proteins (DBPs) play a critical role in the development of drugs for treating genetic diseases and in DNA biology research. It is essential for predicting DNA-binding proteins more accurately and efficiently. In this paper, a Laplacian Local Kernel Alignment-based Restricted Kernel Machine (LapLKA-RKM) is proposed to predict DBPs. In detail, we first extract features from the protein sequence using six methods. Second, the Radial Basis Function (RBF) kernel function is utilized to construct pre-defined kernel metrics. Then, these metrics are combined linearly by weights calculated by LapLKA. Finally, the fused kernel is input to RKM for training and prediction. Independent tests and leave-one-out cross-validation were used to validate the performance of our method on a small dataset and two large datasets. Importantly, we built an online platform to represent our model, which is now freely accessible via http://8.130.69.121:8082/.
Citation: Yuqing Qian, Tingting Shang, Fei Guo, Chunliang Wang, Zhiming Cui, Yijie Ding, Hongjie Wu. Identification of DNA-binding protein based multiple kernel model[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 13149-13170. doi: 10.3934/mbe.2023586
DNA-binding proteins (DBPs) play a critical role in the development of drugs for treating genetic diseases and in DNA biology research. It is essential for predicting DNA-binding proteins more accurately and efficiently. In this paper, a Laplacian Local Kernel Alignment-based Restricted Kernel Machine (LapLKA-RKM) is proposed to predict DBPs. In detail, we first extract features from the protein sequence using six methods. Second, the Radial Basis Function (RBF) kernel function is utilized to construct pre-defined kernel metrics. Then, these metrics are combined linearly by weights calculated by LapLKA. Finally, the fused kernel is input to RKM for training and prediction. Independent tests and leave-one-out cross-validation were used to validate the performance of our method on a small dataset and two large datasets. Importantly, we built an online platform to represent our model, which is now freely accessible via http://8.130.69.121:8082/.
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Figures(7) / Tables(10)
Yuqing Qian, Tingting Shang, Fei Guo, Chunliang Wang, Zhiming Cui, Yijie Ding, Hongjie Wu. Identification of DNA-binding protein based multiple kernel model[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 13149-13170. doi: 10.3934/mbe.2023586
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