Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2017, Article ID 1729301, 9 pages
Research Article

Protein Function Prediction Using Deep Restricted Boltzmann Machines

College of Computer and Information Science, Southwest University, Chongqing, China

Correspondence should be addressed to Guoxian Yu; nc.ude.uws@uyxg

Received 30 March 2017; Accepted 30 May 2017; Published 28 June 2017

Academic Editor: Peter J. Oefner

Copyright © 2017 Xianchun Zou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Accurately annotating biological functions of proteins is one of the key tasks in the postgenome era. Many machine learning based methods have been applied to predict functional annotations of proteins, but this task is rarely solved by deep learning techniques. Deep learning techniques recently have been successfully applied to a wide range of problems, such as video, images, and nature language processing. Inspired by these successful applications, we investigate deep restricted Boltzmann machines (DRBM), a representative deep learning technique, to predict the missing functional annotations of partially annotated proteins. Experimental results on Homo sapiens, Saccharomyces cerevisiae, Mus musculus, and Drosophila show that DRBM achieves better performance than other related methods across different evaluation metrics, and it also runs faster than these comparing methods.