NetGO: improving large-scale protein function prediction with massive network information.

Journal: Nucleic acids research
Published Date: Jul 2, 2019

Abstract

Automated function prediction (AFP) of proteins is of great significance in biology. AFP can be regarded as a problem of the large-scale multi-label classification where a protein can be associated with multiple gene ontology terms as its labels. Based on our GOLabeler-a state-of-the-art method for the third critical assessment of functional annotation (CAFA3), in this paper we propose NetGO, a web server that is able to further improve the performance of the large-scale AFP by incorporating massive protein-protein network information. Specifically, the advantages of NetGO are threefold in using network information: (i) NetGO relies on a powerful learning to rank framework from machine learning to effectively integrate both sequence and network information of proteins; (ii) NetGO uses the massive network information of all species (>2000) in STRING (other than only some specific species) and (iii) NetGO still can use network information to annotate a protein by homology transfer, even if it is not contained in STRING. Separating training and testing data with the same time-delayed settings of CAFA, we comprehensively examined the performance of NetGO. Experimental results have clearly demonstrated that NetGO significantly outperforms GOLabeler and other competing methods. The NetGO web server is freely available at http://issubmission.sjtu.edu.cn/netgo/.

Authors

  • Ronghui You
    School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, China; Center for Computational System Biology, ISTBI, Fudan University, Shanghai 200433, China.
  • Shuwei Yao
    School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China.
  • Yi Xiong
    Departement of Medical Oncology, Lung Cancer and Gastrointestinal Unit, Hunan Cancer Hospital/Affiliated Cancer Hospital of Xiangya School of Medicine, Changsha 410013, China.
  • Xiaodi Huang
    School of Computing and Mathematics, Charles Sturt University, Albury, NSW 2640, Australia.
  • Fengzhu Sun
    Molecular and Computational Biology Program, University of Southern California, Los Angeles, California, USA. fsun@usc.edu.
  • Hiroshi Mamitsuka
    Bioinformatics Center, Institute of Chemical Research, Kyoto University, Uji, Kyoto, 611 - 0011, Japan.
  • Shanfeng Zhu

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