HeCiX: Integrating Knowledge Graphs and Large Language Models for Biomedical Research
<|reference_start|>HeCiX: Integrating Knowledge Graphs and Large Language Models for Biomedical Research: Despite advancements in drug development strategies, 90% of clinical trials fail. This suggests overlooked aspects in target validation and drug optimization. In order to address this, we introduce HeCiX-KG, Hetionet-Clinicaltrials neXus Knowledge Graph, a novel fusion of data from ClinicalTrials.gov and Hetionet in a single knowledge graph. HeCiX-KG combines data on previously conducted clinical trials from ClinicalTrials.gov, and domain expertise on diseases and genes from Hetionet. This offers a thorough resource for clinical researchers. Further, we introduce HeCiX, a system that uses LangChain to integrate HeCiX-KG with GPT-4, and increase its usability. HeCiX shows high performance during evaluation against a range of clinically relevant issues, proving this model to be promising for enhancing the effectiveness of clinical research. Thus, this approach provides a more holistic view of clinical trials and existing biological data.<|reference_end|>
@article{kulkarni2024hecix:,
    title={HeCiX: Integrating Knowledge Graphs and Large Language Models for
  Biomedical Research},
    author={Prerana Sanjay Kulkarni, Muskaan Jain, Disha Sheshanarayana, and
  Srinivasan Parthiban},
    journal={arXiv preprint arXiv:2407.14030},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14030},
    primaryClass={cs.CL cs.AI cs.IR cs.LG}
}