- Pipeline Development: Create a robust pipeline for assessing scGPT's performance in inferring gene regulatory networks (GRNs) from scRNA-seq data.- Evaluation Methods: Employ benchmarks like BEELINE and algorithms such as GENIE3, GRNBoost2, and PIDC to systematically evaluate scGPT's effectiveness in capturing gene regulatory relationships using metrics like precision, recall, F1-score, and AUPR.- Modular Design for Versatility: Design the pipeline with modularity to integrate additional foundation models, enhancing its versatility and extendibility.- Biological Insights and Implications: Deepen understanding of foundation models like scGPT in GRN inference, guiding the development of improved algorithms for deciphering gene regulatory mechanisms from scRNA-seq data, ultimately advancing biological knowledge across various contexts.

- LLM communication between system components - Front end development using Reactjs, LaravelPHP- Server managing using Apache2 Webservices- Backend Services and Laravel integration