title: An approach for identifying cytokines based on a novel ensemble classifier.
abstract: 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.
authors.affiliation: School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China ; Center for Cloud Computing and Big Data, Xiamen University, Xiamen, Fujian, China ; Shanghai Key Laboratory of Intelligent Information Processing, Shanghai, China.
authors.forename: Quan
authors.initials: Q
authors.lastname: Zou
authors.forename: Zhen
authors.initials: Z
authors.lastname: Wang
authors.forename: Xinjun
authors.initials: X
authors.lastname: Guan
authors.forename: Bin
authors.initials: B
authors.lastname: Liu
authors.forename: Yunfeng
authors.initials: Y
authors.lastname: Wu
authors.forename: Ziyu
authors.initials: Z
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry*; D001185:Artificial Intelligence; D001483:Base Sequence; D019295:Computational Biology; D016207:Cytokines / Q000737:chemistry / Q000145:classification* / Q000302:isolation & purification*; D006801:Humans; D012680:Sensitivity and Specificity
chemical_list: D000596:Amino Acids; D016207:Cytokines
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