Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods

DNA-binding proteins play vital roles in cellular processes, such as DNA packaging, replication, transcription, regulation, and other DNA-associated activities. The current main prediction method is based on machine learning, and its accuracy mainly depends on the features extraction method. Therefo...

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Bibliographic Details
Published in:Molecules (Basel, Switzerland) Switzerland), 2017-09, Vol.22 (10), p.1602
Main Authors: Qu, Kaiyang, Han, Ke, Wu, Song, Wang, Guohua, Wei, Leyi
Format: Article
Language:English
Subjects:
Amino Acid Sequence
Computational Biology - methods
Deoxyribonucleic acid
DNA
DNA - chemistry
DNA biosynthesis
DNA-binding protein
DNA-Binding Proteins - metabolism
Gene regulation
Identification methods
Information theory
Learning algorithms
Machine Learning
Methods
mixed feature representation methods
Packaging
Proteins
Representations
Support Vector Machine
Test procedures
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Summary:DNA-binding proteins play vital roles in cellular processes, such as DNA packaging, replication, transcription, regulation, and other DNA-associated activities. The current main prediction method is based on machine learning, and its accuracy mainly depends on the features extraction method. Therefore, using an efficient feature representation method is important to enhance the classification accuracy. However, existing feature representation methods cannot efficiently distinguish DNA-binding proteins from non-DNA-binding proteins. In this paper, a multi-feature representation method, which combines three feature representation methods, namely, K-Skip-N-Grams, Information theory, and Sequential and structural features (SSF), is used to represent the protein sequences and improve feature representation ability. In addition, the classifier is a support vector machine. The mixed-feature representation method is evaluated using 10-fold cross-validation and a test set. Feature vectors, which are obtained from a combination of three feature extractions, show the best performance in 10-fold cross-validation both under non-dimensional reduction and dimensional reduction by max-relevance-max-distance. Moreover, the reduced mixed feature method performs better than the non-reduced mixed feature technique. The feature vectors, which are a combination of SSF and K-Skip-N-Grams, show the best performance in the test set. Among these methods, mixed features exhibit superiority over the single features.
ISSN:1420-3049
1420-3049
DOI:10.3390/molecules22101602