An overview of topic modeling and its current applications in bioinformatics.
An overview of topic modeling and its current applications in bioinformatics.
复制标题
主题建模及其当前在生物信息学中的应用概述
DOI：
10.1186/s40064-016-3252-8
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Zhou W
中科院分区：
文献类型：
--
作者：
Liu L;Tang L;Dong W;Yao S;Zhou W
随着生物数据集的快速积累，迫切需要设计用于自动化数据分析的机器学习方法。近年来，源自自然语言处理领域的主题模型因其可解释性而受到生物信息学领域的广泛关注。我们的目的是回顾生物信息学主题模型的应用和发展。本文从主题模型的描述入手，重点是对主题建模的理解。本文概述了如何在主题模型中构建应用程序以及如何开发主题模型。同时，对主题模型在生物数据中的应用进行了深入的文献检索和分析。根据模型的类型和文档-主题-词概念与生物对象的相似性（以及主题模型的任务），对相关研究进行了分类，并对主题模型在生物信息学应用开发中的应用进行了展望。主题建模是一种有用的方法（与生物信息学中传统的数据约简方法相比），可以提高研究人员解释生物信息的能力。然而，由于缺乏针对特定生物数据优化的主题模型，生物数据主题建模的研究仍然任重道远。我们认为主题模型在生物信息学研究中具有广泛的应用前景。
With the rapid accumulation of biological datasets, machine learning methods designed to automate data analysis are urgently needed. In recent years, so-called topic models that originated from the field of natural language processing have been receiving much attention in bioinformatics because of their interpretability. Our aim was to review the application and development of topic models for bioinformatics. This paper starts with the description of a topic model, with a focus on the understanding of topic modeling. A general outline is provided on how to build an application in a topic model and how to develop a topic model. Meanwhile, the literature on application of topic models to biological data was searched and analyzed in depth. According to the types of models and the analogy between the concept of document-topic-word and a biological object (as well as the tasks of a topic model), we categorized the related studies and provided an outlook on the use of topic models for the development of bioinformatics applications. Topic modeling is a useful method (in contrast to the traditional means of data reduction in bioinformatics) and enhances researchers’ ability to interpret biological information. Nevertheless, due to the lack of topic models optimized for specific biological data, the studies on topic modeling in biological data still have a long and challenging road ahead. We believe that topic models are a promising method for various applications in bioinformatics research.
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