The Use of Large Language Models (LLMs) for Discovering Diseases Associated with Specific Genes

The intricate relationship between genetic variation and human diseases has long been an area of focus in medical research. The identification of risk genes for specific diseases has provided valuable insights into disease mechanisms and potential treatment strategies. However, the process of manually extracting information from literature databases to find disease-gene associations is time-consuming and often lacks real-time updates.

Advancements in genome sequencing techniques have significantly improved our ability to detect genetic markers associated with diseases. However, the vast amount of genetic data generated presents a challenge in translating these findings into actionable insights for clinical decision-making and early risk assessment. This is where the use of Large Language Models (LLMs) comes into play.

The Potential of LLMs in Disease Identification

LLMs, such as OpenAI’s GPT-3, have shown immense potential in understanding and generating human language. These models can be trained on large amounts of text data from diverse sources, including scientific literature. By leveraging the power of LLMs, researchers can develop frameworks to automate the labor-intensive process of sifting through medical literature for evidence linking genetic variations to diseases.

The proposed framework described in this paper aims to utilize LLMs to conduct literature searches and summarize relevant findings. By inputting specific genes as prompts, the framework can extract information from a vast array of scientific literature, identify associations with diseases, and generate a summary of the findings.

The Impact on Disease Diagnosis and Clinical Decision-Making

The efficient identification of diseases associated with specific genetic variations can have a profound impact on disease diagnosis and clinical decision-making. This framework has the potential to accelerate the diagnostic process by providing clinicians with up-to-date information on the associations between genetic variations and diseases.

Additionally, by automating the literature retrieval and summarization process, this framework can save researchers valuable time and resources. It can provide a comprehensive overview of the current scientific knowledge regarding disease-gene associations, enabling researchers to focus on further investigations and potential therapeutic interventions.

Potential Challenges and Future Directions

While the use of LLMs for disease identification offers exciting possibilities, there are several challenges that need to be addressed. Firstly, the quality and accuracy of information obtained from LLM-generated summaries need to be validated against curated databases and expert consensus.

Furthermore, the framework should be continuously updated to ensure that it leverages the latest advancements in genetic research. As new studies and publications emerge, the framework should adapt and incorporate these findings to provide clinicians and researchers with the most current information.

In the future, it will also be interesting to explore the integration of LLM-powered frameworks with other genomic analysis tools. Combining the power of LLMs with computational approaches can enhance our understanding of the functional consequences of genetic variations and their impact on disease development.


The development and application of a framework utilizing Large Language Models (LLMs) for the discovery of diseases associated with specific genes have the potential to revolutionize disease identification and clinical decision-making. By automating the literature retrieval and summarization process, this framework can save time and resources while providing clinicians and researchers with up-to-date and relevant information. However, further research is required to validate the accuracy of LLM-generated summaries and to continuously update the framework to incorporate new scientific findings. In combination with other genomic analysis tools, LLM-powered frameworks may pave the way for a deeper understanding of genetic variations and their role in human diseases.

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