自我相互作用蛋白(SIP)在细胞中最重要的分子过程的执行中起着重要作用,如信号转导,基因表达调控,免疫反应和酶激活。虽然传统的实验方法可以用来生成SIP数据,仅基于生物技术,这是非常昂贵和耗时的。因此,因此,开发一种高效的SIPs检测计算方法显得尤为重要和迫切。在这项研究中,我们通过将位置特定评分矩阵(PSSM)上的Zernike矩(ZM)描述符与概率分类向量机(PCVM)和堆叠稀疏自动编码器(SSAE)相结合,提出了一种基于机器学习技术的新型SIP识别方法。更具体地说,首先利用一种称为ZM的高效特征提取技术在位置特定评分矩阵(PSSM)上生成特征向量;然后,深度神经网络用于降低特征维数和噪声;最后,概率分类向量机用于执行分类。通过交叉验证,在S.erevisiae和HumanSIP数据集上评估了所提出方法的预测性能。实验结果表明,该方法可以达到92.55%和97.47%的较好的准确率,分别。为了进一步评估我们方案对SIP预测的优势,我们还在相同的数据集上比较了PCVM分类器与支持向量机(SVM)和其他现有技术。比较结果表明,所提出的策略优于其他方法,可以作为识别SIP的工具。
Self-interacting proteins (SIPs) play a significant role in the execution of most important molecular processes in cells, such as signal transduction, gene expression regulation, immune response and enzyme activation. Although the traditional experimental methods can be used to generate SIPs data, it is very expensive and time-consuming based only on biological technique. Therefore, it is important and urgent to develop an efficient computational method for SIPs detection. In this study, we present a novel SIPs identification method based on machine learning technology by combing the Zernike Moments (ZMs) descriptor on Position Specific Scoring Matrix (PSSM) with Probabilistic Classification Vector Machines (PCVM) and Stacked Sparse Auto-Encoder (SSAE). More specifically, an efficient feature extraction technique called ZMs is firstly utilized to generate feature vectors on Position Specific Scoring Matrix (PSSM); Then, Deep neural network is employed for reducing the feature dimensions and noise; Finally, the Probabilistic Classification Vector Machine is used to execute the classification. The prediction performance of the proposed method is evaluated on S.erevisiae and Human SIPs datasets via cross-validation. The experimental results indicate that the proposed method can achieve good accuracies of 92.55% and 97.47%, respectively. To further evaluate the advantage of our scheme for SIPs prediction, we also compared the PCVM classifier with the Support Vector Machine (SVM) and other existing techniques on the same data sets. Comparison results reveal that the proposed strategy is outperforms other methods and could be a used tool for identifying SIPs.