Automated Gene Identification in Oncology Literature: A Comparative Evaluation of Natural Language Processing...

The exponential growth of biomedical literature necessitates automated approaches for extracting biological entities, such as genes, to support research. This study systematically compares rule-based, Named Entity Recognition (NER)-based, and transformer-based models for extracting 161 Oncomine™ genes from 100 randomly selected cancer-related abstracts. The transformer-based BioBERT model achieved the highest recall (1.00) and F1-score (0.98), followed by GPT-4o, which, despite its effectiveness, required substantial computational resources. NER-based SciSpaCy models exhibited varying performance, while rule-based string-matching demonstrated high precision but lower recall. The finding highlights the trade-offs between accuracy and computational efficiency, emphasizing the potential for hybrid approaches in large-scale text mining applications.