AI-DRIVEN CLUSTERING OF MEDICAL GENOMICS DATA FOR DISEASE DISCOVERY

Abstract

The integration of clustering algorithms of artificial intelligence techniques, integration of the latter with the field of medical genomics has yielded meaningful advancements in identifying potential disease subgroups as well as improving the precision medicine. The approach of this research is the use of AI driven clustering models to analyze high dimension genomic data to enhance disease discovery. Here study is used to find latent disease clusters and subpopulations with patients by employing semi-supervised learning techniques, unsupervised machine learning frameworks and affinity network fusion. In this method, multiomics data integration, deep learning models for features extraction and graph based clustering methods are combined to increase the accuracy. Other AI models like digital organisms and AI whole whole sequencing are investigated to improve the prediction of the disease biomarkers and genetic interactions. Experimental results show that the proposed AI clustering framework achieves better performance in pattern detection of rare disease and precision of diagnosing than traditional methods. Furthermore, with the integration of AI technologies, drug discovery and biomarker identification for the complex conditions such as glioblastoma and cancer can be fastened. These findings can serve as a strong backing for further improvement of clinical practices and will be beneficial in the areas of precision medicine and personal healthcare solutions. AI able to accelerate deep understanding of multi-omics analysis and genomic research and also leading to more precise and effective diagnostic services, treatment strategies and more insight on new therapeutics.