Uncovering the Effects of Genes, Proteins, and Medications on Functions of Wound Healing: A Dependency Rule-Based Text Mining Approach Leveraging GPT-4 based Evaluation

Wound healing is a complex biological process characterized by intricate cellular and molecular interactions. Understanding the underlying mechanisms and the effects of different biological entities, such as genes, proteins, and medications, on the cellular and biological functions of wound healing is of paramount importance for the development of effective therapeutic interventions. In this paper, we present a text-mining approach aimed to explore and unravel the complex regulatory relationships of genes, proteins, and medications with the biological mechanisms of wound healing. Our approach relies on a set of predefined dependency rules to capture the relationships between biological entities and their target functions from text. By leveraging advanced AI technology like Generative Pre-trained Transformer 4 (GPT-4), also known as ChatGPT, we evaluate the accuracy and quality of the extracted relations. We present a detailed discussion of the encouraging preliminary results that validate the efficacy of our model in identifying potential therapeutic targets in the complex biological system.