College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, 100124, China.
College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, 100124, China.
Comput Biol Chem. 2019 Dec;83:107088. doi: 10.1016/j.compbiolchem.2019.107088. Epub 2019 Jul 6.
ncRNA-protein interactions (ncRPIs) play an important role in a number of cellular processes, such as post-transcriptional modification, transcriptional regulation, disease progression and development. Since experimental methods are expensive and time-consuming to identify the ncRPIs, we proposed a computational method, Deep Mining ncRNA-Protein Interactions (DM-RPIs), for identifying the ncRPIs. In order to descending dimension and excavating hidden information from k-mer frequency of RNA and protein sequences, using the Deep Stacking Auto-encoders Networks (DSANs) model refined the raw data. Three common machine learning algorithms, Support Vector Machine (SVM), Random Forest (RF), and Convolution Neural Network (CNN), were separately trained as individual predictors and then the three individual predictors were integrated together using stacked ensembling strategy. Based on the RPI2241 dataset, DM-RPI obtains an accuracy of 0.851, precision of 0.852, sensitivity of 0.873, specificity of 0.826, and MCC of 0.701, which is promising and pioneering for the prediction of ncRPIs.
ncRNA-蛋白质相互作用（ncRPIs）在许多细胞过程中发挥着重要作用，例如转录后修饰、转录调控、疾病进展和发育。由于实验方法昂贵且耗时，难以识别 ncRPIs，因此我们提出了一种计算方法，即深度挖掘 ncRNA-蛋白质相互作用（DM-RPIs），用于识别 ncRPIs。为了降低维度并从 RNA 和蛋白质序列的 k-mer 频率中挖掘隐藏信息，使用深度堆叠自动编码器网络（DSANs）模型对原始数据进行了细化。分别使用三种常见的机器学习算法（支持向量机（SVM）、随机森林（RF）和卷积神经网络（CNN））作为独立的预测器进行训练，然后使用堆叠集成策略将这三个独立的预测器集成在一起。基于 RPI2241 数据集，DM-RPI 的准确率为 0.851，精度为 0.852，灵敏度为 0.873，特异性为 0.826，MCC 为 0.701，这对于 ncRPIs 的预测具有很大的潜力和开创性。
