A miRNA-Target Prediction Case Study

Giansanti, V., Castelli, M., Beretta, S., & Merelli, I. (2019). Comparing Deep and Machine Learning Approaches in Bioinformatics: A miRNA-Target Prediction Case Study. In V. V. Krzhizhanovskaya, M. H. Lees, P. M. A. Sloot, J. J. Dongarra, J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, ... R. Lam (Eds.), Computational Science – ICCS 2019: 19th International Conference, Faro, Portugal, June 12–14, 2019, Proceedings, Part III (pp. 31-44). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11538 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-22744-9_3MicroRNAs (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. Here, we apply two deep learning classifiers and three different machine learning models to two different miRNA-mRNA datasets, of predictions from 3 different tools: TargetScan, miRanda, and RNAhybrid. Although an experimental validation of the results is needed to better confirm the predictions, deep learning techniques achieved the best performance when the evaluation scores are taken into account.authorsversionpublishe

Mental Representation and Motor Control - Buildings Blocks of Performance in Memory and Brain

Schack T. Mental Representation and Motor Control - Buildings Blocks of Performance in Memory and Brain. In: Hackfort D, ed. Aspire. 2008

Attention and neurocognition

Essig K, Janelle C, Borgo F, Koester D. Attention and neurocognition. In: Papaioannou AG, Hackfort D, eds. Routledge Companion to Sport and Exercise Psychology : Global perspectives and fundamental concepts. International perspectives on key issues in sport and exercise psychology. London: Routledge; 2014: 253--271

Elements and Construction of Motor Control

Schack T, Bläsing B, Hughes C, Flash T, Schilling M. Elements and Construction of Motor Control. In: Papaioannou A, Hackfort D, eds. Routledge Companion to Sport and Exercise Psychology:Global Perspectives and Fundamental Concepts. Routledge, London; 2014: 306-321