Time and Date: 16:30 - 18:10 on 13th June 2019
Chair: Mario Cannataro
|38|| Parallelization of an algorithm for automatic classification of medical data [abstract]
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.
|Victor M. Garcia-Molla, Addisson Salazar, Gonzalo Safont, Antonio M. Vidal and Luis Vergara|
|146|| Comparing Deep and Machine Learning approaches in bioinformatics: a miRNA-target prediction case study [abstract]
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.
|Mauro Castelli, Stefano Beretta, Valentina Giansanti and Ivan Merelli|