BIOMedical Search Engine Framework: Lightweight and customized implementation of domain-specific biomedical search engines

Abstract

The BIOMedical Search Engine Framework supports the development of domain-specific search engines. The key strengths of the framework are modularity and extensibilityin terms of software design, the use of open-source consolidated Web technologies, and the ability to integrate any number of biomedical text mining tools and information resources. Currently, the Smart Drug Search keeps over 1,186,000 documents, containing more than 11,854,000 annotations for 77,200 different concepts. The Smart Drug Search is publicly accessible at http://sing.ei.uvigo.es/sds/. The BIOMedical Search Engine Framework is freely available for non-commercial use at https://github.com/agjacome/biomsef.

PubMed and beyond: biomedical literature search in the age of artificial intelligence

“…This article differs from previous surveys on biomedical literature search tools 9 , 10 , 11 , 12 in three important aspects: (1) We organize the literature search tools according to specific user scenarios and information needs; (2) Our study includes many new systems not covered by previous surveys; (3) Beyond surveying current systems, we also cover practical considerations and best practices of using these tools; (4) We share our perspective on the development of next-generation biomedical literature search engines, especially how large language models (LLM) such as ChatGPT could be utilised to improve the discussed search scenarios. Our goal is to provide a comprehensive overview of specialised literature search tools for researchers and clinicians, which enables more effective exploration of biomedical information and higher-quality care for their patients.…”

DES-ROD: Exploring Literature to Develop New Links between RNA Oxidation and Human Diseases

“…Other tools include Twister that is aimed at reducing the screening time of systematic literature reviews [37]; SWIFT-Review, which is a workbench for systematic review based on NLP [38]; SparkText, which is a big data framework for mining biomedical literature [39]; and GIS, which is an NLP-based framework for gene discovery from scientific literature [40]. In addition to these tools, several frameworks for mining biomedical literature have been developed [41][42][43][44][45][46][47].…”