基于 DNA 结合蛋白的多核模型识别。
Identification of DNA-binding protein based multiple kernel model.
机构信息
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.
出版信息
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。
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