Abstract: Techniques for classifying biomedical image data using a graph neural network are disclosed. In one particular embodiment, the techniques may be realized as a method for classifying biomedical image data comprising generating an annotated representation of biomedical image data; identifying a plurality of pixel clusters based on the biomedical image data; constructing a graph based on the plurality of pixel clusters; determining at least one biomedical feature for at least one node of the graph based on the annotated representation of the biomedical image data; and processing a graph neural network to classify the biomedical image data based on the at least one biomedical feature.

Abstract: The disclosure relates to methods for increasing the speed and efficiency of computational genomics. In particular, the disclosure relates to methods of scaling computational genomics by using one or more specialized architectures for highly parallelized computations, such as graphics processing units (GPUs), tensor processing units (TPUs), and field programmable gate arrays (FPGAs), and the like, to compute the computational genomics calculations.