ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2018, Vol. 55 ›› Issue (8): 1674-1682.doi: 10.7544/issn1000-1239.2018.20180361

Special Issue: 2018数据挖掘前沿进展专题

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Predicting Biological Functions of G Protein-Coupled Receptors Based on Fast Multi-Instance Multi-Label Learning

Wu Jiansheng1, Feng Qiaoyu2, Yuan Jingzhou1, Hu Haifeng2, Zhou Jiate1,Gao Hao1   

  1. 1(School of Geographic and Biological Information, Nanjing University of Posts and Telecommunications, Nanjing 210023);2(School of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003)
  • Online:2018-08-01

Abstract: G protein-coupled receptors (GPCRs) constitute the largest family among human membrane proteins which are the important targets of many drugs on the market. An accurate annotation of the biological functions of GPCR proteins is key to understand their involved biological processes and drug-acting mechanisms. In our previous work, we found that protein function prediction problem can be formulated as a multi-instance multi-label learning (MIML) task. In this paper, we propose a novel method for predicting biological functions of G protein-coupled receptors by using a fast MIML learning called MIMLfast along with a hybrid feature. The hybrid feature consists of amino acid triple information, amino acid correlation information, evolutionary information, secondary structure correlation information, signal peptide information, disordered residue information, physical and chemical properties among GPCR domains. The experimental results show that our method achieves good performance which is superior to state-of-the-art multi-instance multi-label learning methods, multi-label learning methods and CAFA protein function prediction methods.

Key words: G protein-coupled receptors (GPCRs), predicting biological functions, fast multi-instance multi-label learning, domains, hybrid feature

CLC Number: