Capsule-LPI: a LncRNA–protein interaction predicting tool based on a capsule network

BMC Bioinformatics

Ying Li¹  Hang Sun¹  Qi Zhang¹  Shiyao Feng¹  Wei Du¹  Siyu Han² 

[1] Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Qianjin Street, 130012, Changchun, China;Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Qianjin Street, 130012, Changchun, China;Department of Computer Science, Faculty of Engineering, University of Bristol, BS8 1UB, Bristol, UK;

关键词: Long noncoding RNA;    lncRNA–protein interaction;    Capsule network;   

DOI  :  10.1186/s12859-021-04171-y

来源: Springer

【 摘 要 】

BackgroundLong noncoding RNAs (lncRNAs) play important roles in multiple biological processes. Identifying LncRNA–protein interactions (LPIs) is key to understanding lncRNA functions. Although some LPIs computational methods have been developed, the LPIs prediction problem remains challenging. How to integrate multimodal features from more perspectives and build deep learning architectures with better recognition performance have always been the focus of research on LPIs.ResultsWe present a novel multichannel capsule network framework to integrate multimodal features for LPI prediction, Capsule-LPI. Capsule-LPI integrates four groups of multimodal features, including sequence features, motif information, physicochemical properties and secondary structure features. Capsule-LPI is composed of four feature-learning subnetworks and one capsule subnetwork. Through comprehensive experimental comparisons and evaluations, we demonstrate that both multimodal features and the architecture of the multichannel capsule network can significantly improve the performance of LPI prediction. The experimental results show that Capsule-LPI performs better than the existing state-of-the-art tools. The precision of Capsule-LPI is 87.3%, which represents a 1.7% improvement. The F-value of Capsule-LPI is 92.2%, which represents a 1.4% improvement.ConclusionsThis study provides a novel and feasible LPI prediction tool based on the integration of multimodal features and a capsule network. A webserver (http://csbg-jlu.site/lpc/predict) is developed to be convenient for users.

【 授权许可】

CC BY