Protein Function Prediction Using Deep Restricted Boltzmann Machines
Joint Authors
Zou, Xianchun
Wang, Guijun
Yu, Guoxian
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-06-28
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zou, Xianchun& Wang, Guijun& Yu, Guoxian. 2017. Protein Function Prediction Using Deep Restricted Boltzmann Machines. BioMed Research International،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1134143
Modern Language Association (MLA)
Zou, Xianchun…[et al.]. Protein Function Prediction Using Deep Restricted Boltzmann Machines. BioMed Research International No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1134143
American Medical Association (AMA)
Zou, Xianchun& Wang, Guijun& Yu, Guoxian. Protein Function Prediction Using Deep Restricted Boltzmann Machines. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1134143
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1134143