College of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.
School of Computer Science and Engineering, Central South University, Changsha, China.
Math Biosci Eng. 2023 Jun 6;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/.
DNA 结合蛋白（DBPs）在治疗遗传疾病的药物开发和 DNA 生物学研究中起着至关重要的作用。准确高效地预测 DNA 结合蛋白至关重要。在本文中，提出了一种基于拉普拉斯局部核对准的限制核机器（LapLKA-RKM）来预测 DBPs。具体来说，我们首先使用六种方法从蛋白质序列中提取特征。其次，利用径向基函数（RBF）核函数构建预定义的核度量。然后，通过 LapLKA 计算的权重对这些度量进行线性组合。最后，将融合核输入 RKM 进行训练和预测。在一个小数据集和两个大数据集上，使用独立测试和留一法交叉验证来验证我们方法的性能。重要的是，我们构建了一个在线平台来表示我们的模型，现在可以通过 http://8.130.69.121:8082/ 免费访问。
