学习排序在生物信息学任务中的应用。

Application of learning to rank in bioinformatics tasks.

机构信息

Department of Computer Science, University of Tsukuba, Tsukuba, Japan, 3058577.

Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China, 610054.

出版信息

Brief Bioinform. 2021 Sep 2;22(5). doi: 10.1093/bib/bbaa394.

DOI:10.1093/bib/bbaa394

PMID:33454758

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.

摘要

在过去几十年中，学习排序（LTR）算法已逐渐应用于生物信息学。这些方法在该领域的多个研究任务中显示出显著的优势。因此，有必要对这些算法的应用进行总结和讨论，以便这些算法更加方便，并有助于生物信息学的发展。本文基于 LTR 算法在生物信息学中的多种应用，分析了 LTR 算法的特点及其相对于其他类型算法的优势。最后，本文进一步讨论了 LTR 算法的不足之处、更好地利用算法的方法和手段以及当前存在的一些开放性问题。