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Application of learning to rank in bioinformatics tasks.
Ru, Xiaoqing; Ye, Xiucai; Sakurai, Tetsuya; Zou, Quan.
Afiliação
  • Ru X; Department of Computer Science, University of Tsukuba, Tsukuba, Japan, 3058577.
  • Ye X; Department of Computer Science, University of Tsukuba, Tsukuba, Japan, 3058577.
  • Sakurai T; Department of Computer Science, University of Tsukuba, Tsukuba, Japan, 3058577.
  • Zou Q; Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China, 610054.
Brief Bioinform ; 22(5)2021 09 02.
Article em En | MEDLINE | ID: mdl-33454758
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Software / DNA / Proteínas / Drogas em Investigação / Biologia Computacional Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Software / DNA / Proteínas / Drogas em Investigação / Biologia Computacional Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article