AI-Driven Approaches to Bioinformatics for Precision Medicine in Healthcare Systems

As a result of our research, we provide an AIbased bioinformatics framework. The system's purpose is to improve precision medicine by combining omics data with machine learning models and ensemble optimization methods. The suggested Bioformer-Hybrid method uses pipelines for dimensionality reduction, normalization, and feature augmentation to look at biological data before using transformer-based learning to make clinical predictions. The model does better than its rivals, with results like 0.94 [0.93-0.95], 0.92[0.90-0.93], an F1-score of 0.89, and 0.90 for sensitivity and 0.88 for specificity. These results are far better than those of well-known models like FT-Transformer and GraphSAGE. The model underwent a thorough review due to the integration of genomic, proteomic, and clinical data. This approach may be used on a wide range of people, including those of different ages, genders, and places, because it has a consistently high AUROC performance (0.91-0.93). As a consequence, people from all walks of life may benefit from it. The model is a great choice for therapeutic applications that are utilized in the real world since it offers many useful features, such as being easy to understand, having a small file size, and being able to be used in many different ways. These traits may help with many parts of healthcare, including making sure diagnoses are correct, tailoring therapy to each patient, and making decisions.

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