The field of biomedical analytics and protein understanding is rapidly evolving, with a focus on developing innovative methods for real-time health analytics, protein function prediction, and biomedical information retrieval. Recent developments have highlighted the potential of large language models (LLMs) and graph neural networks in advancing our understanding of complex biological systems. Notably, the integration of LLMs with domain-specific knowledge and databases has shown promise in improving the accuracy and efficiency of biomedical information retrieval and protein interaction prediction. Furthermore, the development of novel frameworks and benchmarks for evaluating the performance of LLMs in specialized scientific domains has enabled systematic progress toward reliable computational tools for metabolomics research.

Some noteworthy papers in this area include: The paper on Protein as a Second Language for LLMs, which introduces a novel framework for reformulating amino-acid sequences as sentences in a symbolic language that LLMs can interpret. The paper on BioMedSearch, which presents a multi-source biomedical information retrieval framework based on LLMs that integrates literature retrieval, protein database, and web search access to support accurate and efficient handling of complex biomedical queries.