Abstract: In this paper, we present the optimization and parallelization of a state-of-the-art algorithm for automatic classification, aiming to perform real time classification of clinical data. The parallelization has been carried out so that the algorithm can be used in real time in standard computers, or in high performance computing servers. The fastest versions have been obtained carrying out most of the computations in Graphics processing Units (GPUs). The algorithms obtained have been tested in a case of automatic classification of electroencephalographic signals from patients.

Abstract: MicroRNAs (miRNAs) are small non-coding RNAs with a key role in the post-transcriptional gene expression regularization, thanks to their ability to link with the target mRNA through the complementary base pairing mechanism. Given their role, it is important to identify their targets and, to this purpose, different tools were proposed to solve this problem. However, their results can be very different, so the community is now moving toward the deployment of integration tools, which should be able to perform better than the single ones. As Machine and Deep Learning algorithms are now in their popular years, we developed different classifiers from both areas to verify their ability to recognize possible miRNA-mRNA interactions and evaluated their performance, showing the potentialities and the limits that those algorithms have in this field.