Artificial Neural Network Approach to Prediction of Protein-RNA Residue-base Contacts

Morihiro Hayashida, Jose Nacher, Hitoshi Koyano

2019

Abstract

Protein-RNA complexes play essential roles in a cell, and are involved in the post-transcriptional regulation of gene expression. Therefore, it is important to analyze and elucidate structures of protein-RNA complexes and also contacts between residues and bases in their interactions. A method based on conditional random fields (CRFs) was developed for predicting residue-base contacts using evolutionary relationships between individual positions of a residue and a base. Further, the probabilistic model was modified to improve the prediction accuracy. Recently, many researchers focus on deep neural networks due to its classification performance. In this paper, we develop a neural network with five layers for predicting residue-base contacts. From computational experiments, in terms of the area under the receiver operating characteristic curve (AUC), the predictive performance of our proposed method was comparable or better than those of the CRF-based methods.

Paper Citation

in Harvard Style

Hayashida M., Nacher J. and Koyano H. (2019). Artificial Neural Network Approach to Prediction of Protein-RNA Residue-base Contacts. In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 3: BIOINFORMATICS; ISBN 978-989-758-353-7, SciTePress, pages 163-167. DOI: 10.5220/0007348101630167

in Bibtex Style

@conference{bioinformatics19,
author={Morihiro Hayashida and Jose Nacher and Hitoshi Koyano},
title={Artificial Neural Network Approach to Prediction of Protein-RNA Residue-base Contacts},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 3: BIOINFORMATICS},
year={2019},
pages={163-167},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007348101630167},
isbn={978-989-758-353-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 3: BIOINFORMATICS
TI - Artificial Neural Network Approach to Prediction of Protein-RNA Residue-base Contacts
SN - 978-989-758-353-7
AU - Hayashida M.
AU - Nacher J.
AU - Koyano H.
PY - 2019
SP - 163
EP - 167
DO - 10.5220/0007348101630167
PB - SciTePress