Authors:
Morihiro Hayashida
1
;
Jose Nacher
2
and
Hitoshi Koyano
3
Affiliations:
1
Department of Electrical Engineering and Computer Science, National Institute of Technology, Matsue College, Matsue, Shimane, Japan
;
2
Department of Information Science, Faculty of Science, Toho University, Funabashi, Chiba, Japan
;
3
School of Life Science and Technology, Tokyo Institute of Technology, Meguro-ku, Tokyo, Japan
Keyword(s):
Fully Connected Neural Network, Protein-RNA Interaction, Residue-base Contact.
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.