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LitSuggest: a web-based system for literature recommendation and curation using machine learning by Alexis Allot; Kyubum Lee; Qingyu Chen; Ling Luo; Zhiyong Lu is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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## Abstract Searching and reading relevant literature is a routine practice in biomedical research. However, it is challenging for a user to design optimal search queries using all the keywords related to a given topic. As such, existing search systems such as PubMed often return suboptimal results. Several computational methods have been proposed as an effective alternative to keyword-based query methods for literature recommendation. However, those methods require specialized knowledge in machine learning and natural language processing, which can make them difficult for biologists to utilize. In this paper, we propose LitSuggest, a web server that provides an all-in-one literature recommendation and curation service to help biomedical researchers stay up to date with scientific literature. LitSuggest combines advanced machine learning techniques for suggesting relevant PubMed articles with high accuracy. In addition to innovative text-processing methods, LitSuggest offers multiple advantages over existing tools. First, LitSuggest allows users to curate, organize, and download classification results in a single interface. Second, users can easily fine-tune LitSuggest results by u

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Author

Alexis Allot; Kyubum Lee; Qingyu Chen; Ling Luo; Zhiyong Lu

Publisher

Oxford University Press (OUP)

Published

2021

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)