HeCiX: Integrating Knowledge Graphs and Large Language Models for Biomedical Research
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909262150631424 |
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| author | Kulkarni, Prerana Sanjay Jain, Muskaan Sheshanarayana, Disha Parthiban, Srinivasan |
| author_facet | Kulkarni, Prerana Sanjay Jain, Muskaan Sheshanarayana, Disha Parthiban, Srinivasan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14030 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HeCiX: Integrating Knowledge Graphs and Large Language Models for Biomedical Research Kulkarni, Prerana Sanjay Jain, Muskaan Sheshanarayana, Disha Parthiban, Srinivasan Computation and Language Artificial Intelligence Information Retrieval Machine Learning 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. |
| title | HeCiX: Integrating Knowledge Graphs and Large Language Models for Biomedical Research |
| topic | Computation and Language Artificial Intelligence Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2407.14030 |