Harnessing deep learning for the discovery of latent patterns in multi-omics medical data
The rapid growth of biomedical data, particularly multi-omics data including genomes,transcriptomics, proteomics, metabolomics, and epigenomics, medical research and clinicaldecision-making confront both new opportunities and obstacles. The huge and diversifiednature of these datasets cannot always be managed using traditional data analysis methods.As a consequence, deep learning has emerged as a strong tool for analysing numerous omicsdata due to its ability to handle complex and non-linear relationships. This paper explores thefundamental concepts of deep learning and how they are used in multi-omics medical datamining. We demonstrate how autoencoders, variational autoencoders, multimodal models,attention mechanisms, transformers, and graph neural networks enable pattern analysis andrecognition across all omics data. Deep learning has been found to be effective in illnessclassification, biomarker identification, gene network learning, and therapeutic efficacyprediction. We also consider critical problems like as data quality, model explainability,whether findings can be repeated, and computational power requirements. We now considerfuture elements of combining omics with clinical and imaging data, explainable AI, federatedlearning, and real-time diagnostics. Overall, this study emphasises the need of collaboratingacross disciplines to advance deep learning-based multi-omics research for precision medicineand comprehending complicated disorders.