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Title: Predicting the interaction biomolecule types for lncRNA: an ensemble deep learning approach
Authors: Zhang, Yu
Jia, Cangzhi
Kwoh, Chee Keong
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Zhang, Y., Jia, C. & Kwoh, C. K. (2021). Predicting the interaction biomolecule types for lncRNA: an ensemble deep learning approach. Briefings in Bioinformatics, 22(4), bbaa228-.
Journal: Briefings in Bioinformatics
Abstract: Long noncoding RNAs (lncRNAs) play significant roles in various physiological and pathological processes via their interactions with biomolecules like DNA, RNA and protein. The existing in silico methods used for predicting the functions of lncRNA mainly rely on calculating the similarity of lncRNA or investigating whether an lncRNA can interact with a specific biomolecule or disease. In this work, we explored the functions of lncRNA from a different perspective: we presented a tool for predicting the interaction biomolecule type for a given lncRNA. For this purpose, we first investigated the main molecular mechanisms of the interactions of lncRNA-RNA, lncRNA-protein and lncRNA-DNA. Then, we developed an ensemble deep learning model: lncIBTP (lncRNA Interaction Biomolecule Type Prediction). This model predicted the interactions between lncRNA and different types of biomolecules. On the 5-fold cross-validation, the lncIBTP achieves average values of 0.7042 in accuracy, 0.7903 and 0.6421 in macro-average area under receiver operating characteristic curve and precision-recall curve, respectively, which illustrates the model effectiveness. Besides, based on the analysis of the collected published data and prediction results, we hypothesized that the characteristics of lncRNAs that interacted with DNA may be different from those that interacted with only RNA.
ISSN: 1467-5463
DOI: 10.1093/bib/bbaa228
Schools: School of Computer Science and Engineering 
Rights: © The Author(s) 2020. Published by Oxford University Press. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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