Searching disease-related genes with Mapping of Biological Entities from Literature (MaBEL)

The exponential growth of biomedical literature presents a major challenge for systematically identifying disease-related genes and therapeutic targets. We introduce MaBEL (Mapping of Biological Entities from Literature), a scalable and adaptable text-mining platform that unifies literature retrieval, entity recognition, and data integration into a single framework. In contrast to single-source text-mining systems, MaBEL retrieves publications from PubMed, Scopus, ScienceDirect, SciELO, and major preprint servers (bioRxiv, medRxiv, arXiv, ChemRxiv), consolidating them through DOI-based deduplication to ensure comprehensive and nonredundant coverage. The platform employs modular natural language processing pipelines, combining SciSpaCy for gene and protein recognition, BioSyn for rapid alias normalization, and PubTator 3.0 for enriched semantic and relational annotation. Built on a distributed architecture using Flask, Celery, and Docker, MaBEL supports asynchronous, large-scale text processing with near real-time performance. Applied to seven major diseases, MaBEL processed over 14,000 unique articles, achieving accurate identification of high-frequency, disease-salient genes and strong concordance with Open Targets Platform association scores. This demonstrates its reliability for uncovering biologically meaningful disease-gene relationships. By integrating multi-source retrieval, scalable computation, and modular adaptability, MaBEL represents a novel, extensible framework that advances biomedical text mining beyond static, single-database approaches, facilitating rapid hypothesis generation and accelerating the discovery of molecular targets in translational research. The source codes can be accessed at https://github.com/omixlab/Mabel.