An overview of topic modeling and its current applications in bioinformatics

By Lin Liu, L. Tang, Wenfu Dong, Shaowen Yao, Wei Zhou. SpringerPlus, 2016

392 citations recorded by the source corpus.

BackgroundWith 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.DescriptionThis 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.ConclusionTopic 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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2392f7f8b64c2016bf57957d51f32b7cef2acbcd

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Citations in the corpus

Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis (2020, background): “Liu et al. (2016) reviewed TM techniques for sentimental analysis.”