Multi-model predictive analysis of RNA solvent accessibility based on modified residual attention mechanism. Issue 6 (27th October 2022)

• Record Type: Journal Article
• Authors: Huang, Yuyao; Luo, Jiesi; Jing, Runyu; Li, Menglong
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 6(2022)
• Publication Date: 2022-10-27
• Subjects: attention value -- residual network -- attention mechanism -- machine learning -- deep learning -- RNA solvent accessibility; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac470
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Predicting RNA solvent accessibility using only primary sequence data can be regarded as sequence-based prediction work. Currently, the established studies for sequence-based RNA solvent accessibility prediction are limited due to the available number of datasets and black box prediction. To improve these issues, we first expanded the available RNA structures and then developed a sequence-based model using modified attention layers with different receptive fields to conform to the stem–loop structure of RNA chains. We measured the improvement with an extended dataset and further explored the model's interpretability by analysing the model structures, attention values and hyperparameters. Finally, we found that the developed model regarded the pieces of a sequence as templates during the training process. This work will be helpful for researchers who would like to build RNA attribute prediction models using deep learning in the future.