A Literature Review of Gene Function Prediction by Modeling Gene Ontology.

Zhao, Yingwen; Wang, Jun; Chen, Jian; Zhang, Xiangliang; Guo, Maozu; Yu, Guoxian

A Literature Review of Gene Function Prediction by Modeling Gene Ontology.

Files

A literature review.pdf (1000.92 KB)

Type

Article

Authors

Zhao, Yingwen
Wang, Jun
Chen, Jian
Zhang, Xiangliang

Guo, Maozu
Yu, Guoxian

KAUST Department

Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Machine Intelligence & kNowledge Engineering Lab

Date

2020-04-24

Submitted Date

2020-01-02

Abstract

Annotating the functional properties of gene products, i.e., RNAs and proteins, is a fundamental task in biology. The Gene Ontology database (GO) was developed to systematically describe the functional properties of gene products across species, and to facilitate the computational prediction of gene function. As GO is routinely updated, it serves as the gold standard and main knowledge source in functional genomics. Many gene function prediction methods making use of GO have been proposed. But no literature review has summarized these methods and the possibilities for future efforts from the perspective of GO. To bridge this gap, we review the existing methods with an emphasis on recent solutions. First, we introduce the conventions of GO and the widely adopted evaluation metrics for gene function prediction. Next, we summarize current methods of gene function prediction that apply GO in different ways, such as using hierarchical or flat inter-relationships between GO terms, compressing massive GO terms and quantifying semantic similarities. Although many efforts have improved performance by harnessing GO, we conclude that there remain many largely overlooked but important topics for future research.

Citation

Zhao, Y., Wang, J., Chen, J., Zhang, X., Guo, M., & Yu, G. (2020). A Literature Review of Gene Function Prediction by Modeling Gene Ontology. Frontiers in Genetics, 11. doi:10.3389/fgene.2020.00400

Acknowledgements

Funding. This work was financially supported by Natural Science Foundation of China (61872300), Fundamental Research Funds for the Central Universities (XDJK2019B024 and XDJK2020B028), Natural Science Foundation of CQ CSTC (cstc2018-jcyjAX0228), and King Abdullah University of Science and Technology, under award number FCC/1/1976-19-01.