Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods

• 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; Transcription
• ISSN: 1420-3049; 1420-3049
• DOI: 10.3390/molecules22101602

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.