PubMed and beyond: a survey of web tools for searching biomedical literature

By Zhiyong Lu. Database J. Biol. Databases Curation, 2011

576 citations recorded by the source corpus.

The past decade has witnessed the modern advances of high-throughput technology and rapid growth of research capacity in producing large-scale biological data, both of which were concomitant with an exponential growth of biomedical literature. This wealth of scholarly knowledge is of significant importance for researchers in making scientific discoveries and healthcare professionals in managing health-related matters. However, the acquisition of such information is becoming increasingly difficult due to its large volume and rapid growth. In response, the National Center for Biotechnology Information (NCBI) is continuously making changes to its PubMed Web service for improvement. Meanwhile, different entities have devoted themselves to developing Web tools for helping users quickly and efficiently search and retrieve relevant publications. These practices, together with maturity in the field of text mining, have led to an increase in the number and quality of various Web tools that provide comparable literature search service to PubMed. In this study, we review 28 such tools, highlight their respective innovations, compare them to the PubMed system and one another, and discuss directions for future development. Furthermore, we have built a website dedicated to tracking existing systems and future advances in the field of biomedical literature search. Taken together, our work serves information seekers in choosing tools for their needs and service providers and developers in keeping current in the field. Database URL: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/search

Subfields: Artificial Intelligence, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bd2062eb413816144bcfc1c8511f73712c67ac74

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Citations in the corpus

A survey of current trends in computational drug repositioning (2016, background): “The biomedical and pharmaceutical knowledge available in literature [70, 71] or databases contains vast amount of information for drugs and diseases, which can be automatically mined [72–74] and retrieved [75], thanks to the recent advances in text mining research [76].”
DNorm: disease name normalization with pairwise learning to rank (2013, methodology): “Our current purpose is to support entity-specific semantic search of the biomedical literature (Lu, 2011) and computer-assisted biocuration, especially document triage (Kim et al. , 2012).”
Detection of drug‐drug interactions through data mining studies using clinical sources, scientific literature and social media (2018, unknown): “that provide literature search service have been developed [152] to help users to make efficient searches and collect relevant data.”
Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health (2023, background): “Information retrieval (IR) is an integral part in clinical decision making [47] and biomedical knowledge acquisition [48], as it covers various information-seeking behaviors such as literature search [49], question answering [50], and article recommendation [51].”
Deep Learning--based Text Classification (2020, background): “PubMed [198] is a search engine developed by the National Library of Medicine for medical and biological scientific papers, which contains a document collection.”
A Survey on Text Classification: From Traditional to Deep Learning (2020, methodology): “The topic labeling datasets include DBPedia [238], Ohsumed [239], Yahoo answers (YahooA) [93], EUR-Lex [240], Amazon670K [241], Bing [244], Fudan [245], and PubMed [261].”
Benchmarking Retrieval-Augmented Generation for Medicine (2024, background): “PubMed 13 is the most widely used literature resource (Lu, 2011; Jin et al., 2024), containing over 36 million biomedical articles.”
Finding useful data across multiple biomedical data repositories using DataMed (2017, background): “The goal is to help users find data from sources that they would be unlikely to encounter otherwise, as PubMed does with the medical literature 4 .”