Abstract Advances in technology and data preprocessing have enabled the generation of diverse and complementary multiomic, multimodal data, with rich information that offers remarkable opportunities to understand biological processes involved in complex diseases and transform medicine. Analyzing multiomics and multimodal data to obtain useful information and knowledge is challenging because the data are complex, heterogeneous, and high-dimensional, requiring a considerable level of analytical sophistication. The amount of data to be handled during training and real data applications can be massive, and traditional central processing units (CPUs) may struggle to handle the size of the data. Graphical Processing Units (GPUs) are essential for handling big data since they offer parallel processing capabilities that can significantly speed up training tasks. The Minnesota Supercomputing Institute (MSI) addresses the high-performance computing needs of research groups at the University of Minnesota, providing excellent resources with free CPU and GPU time, memory, and storage, up to a certain limit. The resources at MSI are shared by the entire research community at the University of Minnesota and have a queue system that limits the number of tasks that can be run, sometimes leading to job submission failures and delays in job execution. Since our simulations for the parent grant are closely linked to real data projects and our applications sometimes require loading large data into memory, we need computing resources that are fast, parallelizable, have a large amount of memory and storage available, and can be executed without delay to speed up our training and testing processes. We are requesting funds to purchase computing resources from MSI specifically for our research group since the MSI allocation is not enough to meet our computational needs. This will eventually improve our training and testing processes. Ultimately, our methods and algorithms to be developed have the potential to narrow the gap from raw molecular data to biological insights, offer opportunity to expand the definition of complex diseases, and allow to stratify patients and identify those who might benefit from targeted interventions.