Application of learning to rank in bioinformatics tasks. Issue 5 (18th January 2021)

• Record Type: Journal Article
• Authors: Ru, Xiaoqing; Ye, Xiucai; Sakurai, Tetsuya; Zou, Quan
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 5(2021)
• Publication Date: 2021-01-18
• Subjects: LTR -- bioinformatics -- multiple research tasks; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa394
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Over the past decades, learning to rank (LTR) algorithms have been gradually applied to bioinformatics. Such methods have shown significant advantages in multiple research tasks in this field. Therefore, it is necessary to summarize and discuss the application of these algorithms so that these algorithms are convenient and contribute to bioinformatics. In this paper, the characteristics of LTR algorithms and their strengths over other types of algorithms are analyzed based on the application of multiple perspectives in bioinformatics. Finally, the paper further discusses the shortcomings of the LTR algorithms, the methods and means to better use the algorithms and some open problems that currently exist.