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A Novel Multi-Label Predictor for Identifying Multi-Functional Classes of Human Membrane Proteins

Volume 14, Number 7, July 2018, pp. 1628-1634
DOI: 10.23940/ijpe.18.07.p27.16281634

Xiao Wang, Guoqing Li, Weiwei Zhang, Hongwei Tao, and Yinghui Meng

School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China

(Submitted on April 4, 2018; Revised on May 17, 2018; Accepted on June 23, 2018)


Knowing which types of functionality that human membrane proteins belong to is very helpful for understanding their functions. However, most existing online prediction methods have some disadvantages, including: 1) they obtain very low prediction accuracy, and 2) they can only predict single-functional classes of cytomembrane proteins in humans. To overcome the drawbacks, a new multi-label predictor, namely mMem-Hum, is proposed. In addition to predicting types of single-function membrane proteins, it can also predict multi-functional types. Specifically, discriminative features of membrane proteins are generated by using amino acid sequence information and evolutionary information, and then they are classified by a new multi-label classifier that utilizes label correlations. Experimental results reveal that the performance of mMem-Hum is significantly better than other existing forecasting methods. This indicates that mMem-Hum may become a promising prediction tool for classifying functional classes of cytomembrane proteins in humans.


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