Journal
GENES
Volume 10, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/genes10110924
Keywords
self-interacting proteins; de novo protein sequence; global vector representation; multi-grained cascade forest
Categories
Funding
- NATIONAL NATURAL SCIENCE FOUNDATION OF CHINA [61373086, 61572506]
- West Light Foundation of The Chinese Academy of Sciences [2017-XBZG-BR-001]
Ask authors/readers for more resources
Self-interacting proteins (SIPs) is of paramount importance in current molecular biology. There have been developed a number of traditional biological experiment methods for predicting SIPs in the past few years. However, these methods are costly, time-consuming and inefficient, and often limit their usage for predicting SIPs. Therefore, the development of computational method emerges at the times require. In this paper, we for the first time proposed a novel deep learning model which combined natural language processing (NLP) method for potential SIPs prediction from the protein sequence information. More specifically, the protein sequence is de novo assembled by k-mers. Then, we obtained the global vectors representation for each protein sequences by using natural language processing (NLP) technique. Finally, based on the knowledge of known self-interacting and non-interacting proteins, a multi-grained cascade forest model is trained to predict SIPs. Comprehensive experiments were performed on yeast and human datasets, which obtained an accuracy rate of 91.45% and 93.12%, respectively. From our evaluations, the experimental results show that the use of amino acid semantics information is very helpful for addressing the problem of sequences containing both self-interacting and non-interacting pairs of proteins. This work would have potential applications for various biological classification problems.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available