AI and Biological Data Fusion: Theoretical Model Construction and Validation Boundary Exploration for Disease Target Prediction

Abstract

This article focuses on the application of the integration of AI and biological data in the field of disease target prediction. Firstly, its significant importance was expounded, and it was pointed out that this integration can enhance the accuracy of prediction and accelerate drug research and development, etc. Then, the key elements for the construction of theoretical models for disease target prediction were discussed in detail, including data preprocessing, feature selection and extraction, and model algorithm selection, etc. Subsequently, the verification boundaries of the theoretical model were discussed, covering aspects such as verification methods, verification metrics, and the assessment of the model's generalization ability. Finally, summarize the full text, emphasize the potential and challenges of the integration of AI and biological data in the prediction of disease targets, and look forward to the future development direction.