LPI-CSFFR: Combining serial fusion with feature reuse for predicting LncRNA-protein interactions. (August 2022)

• Record Type: Journal Article
• Authors: Huang, Xiaoqian; Shi, Yi; Yan, Jing; Qu, Wenyan; Li, Xiaoyi; Tan, Jianjun
• Is Part Of: Computational biology and chemistry. Volume 99(2022)
• Publication Date: 2022-08
• Subjects: Convolution neural network -- Serial fusion -- Feature reuse -- LncRNA-protein interactions; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2022.107718
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: Long non-coding RNAs (LncRNAs) play important roles in a series of life activities, and they function primarily with proteins. The wet experimental-based methods in lncRNA-protein interactions (lncRPIs) study are time-consuming and expensive. In this study, we propose for the first time a novel feature fusion method, the LPI-CSFFR, to train and predict LncRPIs based on a Convolutional Neural Network (CNN) with feature reuse and serial fusion in sequences, secondary structures, and physicochemical properties of proteins and lncRNAs. The experimental results indicate that LPI-CSFFR achieves excellent performance on the datasets RPI1460 and RPI1807 with an accuracy of 83.7 % and 98.1 %, respectively. We further compare LPI-CSFFR with the state-of-the-art existing methods on the same benchmark datasets to evaluate the performance. In addition, to test the generalization performance of the model, we independently test sample pairs of five model organisms, where Mus musculus are the highest prediction accuracy of 99.5 %, and we find multiple hotspot proteins after constructing an interaction network. Finally, we test the predictive power of the LPI-CSFFR for sample pairs with unknown interactions. The results indicate that LPI-CSFFR is promising for predicting potential LncRPIs. The relevant source code and the data used in this study are available at https://github.com/JianjunTan-Beijing/LPI-CSFFR. Graphical Abstract: ga1 Highlights: Reliable negative samples are built to improve the LPI-CSFFR predicting performance. Multi-source information fusion extends the protein and RNA features. Framework combining serial fusion with feature reuse based on the CNN algorithm is utilized for better prediction. LPI-CSFFR is promising for predicting potential interactive pairs and exploring hot spot proteins.