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BioMed Research International
Volume 2013 (2013), Article ID 686090, 11 pages
Research Article

An Approach for Identifying Cytokines Based on a Novel Ensemble Classifier

1School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China
2Center for Cloud Computing and Big Data, Xiamen University, Xiamen, Fujian, China
3Shanghai Key Laboratory of Intelligent Information Processing, Shanghai, China
4School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China

Received 12 May 2013; Revised 2 July 2013; Accepted 15 July 2013

Academic Editor: Lei Chen

Copyright © 2013 Quan 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.


Biology is meaningful and important to identify cytokines and investigate their various functions and biochemical mechanisms. However, several issues remain, including the large scale of benchmark datasets, serious imbalance of data, and discovery of new gene families. In this paper, we employ the machine learning approach based on a novel ensemble classifier to predict cytokines. We directly selected amino acids sequences as research objects. First, we pretreated the benchmark data accurately. Next, we analyzed the physicochemical properties and distribution of whole amino acids and then extracted a group of 120-dimensional (120D) valid features to represent sequences. Third, in the view of the serious imbalance in benchmark datasets, we utilized a sampling approach based on the synthetic minority oversampling technique algorithm and K-means clustering undersampling algorithm to rebuild the training set. Finally, we built a library for dynamic selection and circulating combination based on clustering (LibD3C) and employed the new training set to realize cytokine classification. Experiments showed that the geometric mean of sensitivity and specificity obtained through our approach is as high as 93.3%, which proves that our approach is effective for identifying cytokines.