PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge

Authors  : Chih-Hsuan Wei, Alexis Allot, Po-Ting Lai, Robert Leaman, Shubo Tian, Ling Luo, Qiao Jin, Zhizheng Wang, Qingyu Chen, Zhiyong Lu

PubTator 3.0 ( is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for key concepts like proteins, genetic variants, diseases and chemicals. It currently provides over one billion entity and relation annotations across approximately 36 million PubMed abstracts and 6 million full-text articles from the PMC open access subset, updated weekly.

PubTator 3.0’s online interface and API utilize these precomputed entity relations and synonyms to provide advanced search capabilities and enable large-scale analyses, streamlining many complex information needs. We showcase the retrieval quality of PubTator 3.0 using a series of entity pair queries, demonstrating that PubTator 3.0 retrieves a greater number of articles than either PubMed or Google Scholar, with higher precision in the top 20 results.

We further show that integrating ChatGPT (GPT-4) with PubTator APIs dramatically improves the factuality and verifiability of its responses. In summary, PubTator 3.0 offers a comprehensive set of features and tools that allow researchers to navigate the ever-expanding wealth of biomedical literature, expediting research and unlocking valuable insights for scientific discovery.

URL : PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge


Using AI to solve business problems in scholarly publishing

Author: Michael Upshall

Artificial intelligence (AI) tools are widely used today in many areas, and are now being introduced into scholarly publishing. This article provides a brief overview of present-day AI and machine learning as used for text-based resources such as journal articles and book chapters, and provides an example of its application to identify suitable peer reviewers for manuscript submissions.

It describes how one company, UNSILO, has created a tool for this purpose, and the underlying technology used to deliver it. The article also offers a glimpse into a future where AI will profoundly change the way that academic publishing will work.

URL : Using AI to solve business problems in scholarly publishing


Artificial intelligence in peer review: How can evolutionary computation support journal editors?

Authors : Maciej J. Mrowinski, Piotr Fronczak, Agata Fronczak, Marcel Ausloos, Olgica Nedic

With the volume of manuscripts submitted for publication growing every year, the deficiencies of peer review (e.g. long review times) are becoming more apparent. Editorial strategies, sets of guidelines designed to speed up the process and reduce editors workloads, are treated as trade secrets by publishing houses and are not shared publicly.

To improve the effectiveness of their strategies, editors in small publishing groups are faced with undertaking an iterative trial-and-error approach. We show that Cartesian Genetic Programming, a nature-inspired evolutionary algorithm, can dramatically improve editorial strategies.

The artificially evolved strategy reduced the duration of the peer review process by 30%, without increasing the pool of reviewers (in comparison to a typical human-developed strategy).

Evolutionary computation has typically been used in technological processes or biological ecosystems. Our results demonstrate that genetic programs can improve real-world social systems that are usually much harder to understand and control than physical systems.