EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA–protein interaction prediction

作者：Jingjing Wang, Yanpeng Zhao, Weikang Gong, Yang Liu, Mei Wang, Xiaoqian Huang, Jianjun Tan · 发表于：BMC Bioinformatics · 年份：2021 · DOI：10.1186/s12859-021-04069-9 · 被引用次数：40 · 研究领域：Machine Learning in Bioinformatics、Cancer-related molecular mechanisms research、Bioinformatics and Genomic Networks

BACKGROUND: Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiological and pathological processes. The experimental methods used for predicting ncRNA-protein interactions are time-consuming and labor-intensive. Therefore, there is an increasing demand for computational methods to accurately and efficiently predict ncRNA-protein interactions. RESULTS: In this work, we presented an ensemble deep learning-based method, EDLMFC, to predict ncRNA-protein interactions using the combination of multi-scale features, including primary sequence features, secondary structure sequence features, and tertiary structure features. Conjoint k-mer was used to extract protein/ncRNA sequence features, integrating tertiary structure features, then fed into an ensemble deep learning model, which combined convolutional neural network (CNN) to learn dominating biological information with bi-directional long short-term memory network (BLSTM) to capture long-range dependencies among the features identified by the CNN. Compared with other state-of-the-art methods under five-fold cross-validation, EDLMFC shows the best performance with accuracy of 93.8%, 89.7%, and 86.1% on RPI1807, NPInter v2.0, and RPI488 datasets, respectively. The results of the independent test demonstrated that EDLMFC can effectively predict potential ncRNA-protein interactions from different organisms. Furtherly, EDLMFC is also shown to predict hub ncRNAs and proteins presented in ncRNA-protein n...