HERMES: an open-source mining tool for open-access literature

Abstract

Motivation: The exponential growth of open-access scientific literature presents researchers with unprecedented opportunities but also poses a significant challenge: how to efficiently identify and prioritize relevant publications in a transparent and customizable manner. Existing search engines index large volumes of biomedical literature but rarely provide user-defined ranking options, reproducibility, or integration of domain-specific criteria. This gap is particularly limiting for specialized fields, where nuanced keyword combinations, literature recency, and contextual interpretation are critical. Results: We present HERMES, an open-source literature mining tool for targeted retrieval and ranking of full-text open-access publications from PubMed Central (PMC). HERMES employs a composite scoring algorithm that integrates keyword frequency, citation counts, and publication age to prioritize publications. It further supports summarization, biomedical entity recognition, and PDF report generation. An intuitive graphical user interface (GUI) allows researchers without programming expertise to perform complex literature mining tasks, while multithreaded processing ensures efficiency for large-scale queries. HERMES provides a reproducible and adaptable framework for literature discovery, empowering researchers to rapidly identify relevant literature and promoting transparency and community-driven extension. Availability and implementation: HERMES (version 1.2) is implemented in Python (3.11). The source code is freely available on GitHub at https://github.com/julien-charest/hermes and is distributed under the GPL-3 license.