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arXiv:2405.18605v1 Announce Type: new Abstract: This paper presents a methodology for enhancing relation extraction from biomedical texts, focusing specifically on chemical-gene interactions. Leveraging the BioBERT model and a multi-layer fully connected network architecture, our approach integrates the ChemProt and DrugProt datasets using a novel merging strategy. Through extensive experimentation, we demonstrate significant performance improvements, particularly in CPR groups shared between the datasets. The findings underscore the importance of dataset merging in augmenting sample counts and improving model accuracy. Moreover, the study highlights the potential of automated information extraction in biomedical research and clinical practice. Source link lol

via Shutterstock As the business world eagerly embraces the potential of GenAI, many companies are discovering that there are still lots of challenges along the way. One of the key challenges is complexity.  Companies are struggling to use decision-making tools because they are too complex. This is evident in the lack of growth in the adoption of business intelligence (BI) and data analytics tools. The percentage of employees actively using BI and analytics tools currently stands at only 25% on average.  (Laborant/Shutterstock) Despite advances in technology and investment in literacy programs to educate employees on the use of data, the…