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Recent Publications

Lauro Moraes, Pedro Silva, Eduardo Luz, and Gladston Moreira. 2022. “CapsProm: A capsule network for promoter prediction.” Computers in Biology and Medicine, Pp. 105627. Publisher's VersionAbstract
Locating the promoter region in DNA sequences is of paramount importance in bioinformatics. This problem has been widely studied in the literature, but it has not yet been fully resolved. Some researchers have shown remarkable results using convolutional networks that allowed the automatic extraction of features from a DNA chain. However, a single architecture schema that could learn the promoter prediction task competitively for several organisms has not yet been achieved. Thus, researchers must seek new architectures by hand-designing or by Neural Architecture Search for each new evaluated organism dataset. This work proposes a versatile architecture based on a capsule network that can accurately identify promoter sequences in raw DNA data from five different organisms, eukaryotic and prokaryotic. Our architecture, the CapsProm, could help create models with minimum effort to learn the promoter identification task between different datasets. Furthermore, the CapsProm showed competitive results, overcoming the baseline method in five out of seven tested datasets (F1-score). The models and source code are made available at https://github.com/lauromoraes/CapsNet-promoter.
Guilherme Silva., Pedro Silva., Mariana Mota., Eduardo Luz., and Gladston Moreira. 2022. “An Efficient Contact Lens Spoofing Classification.” In Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1: ICEIS,, Pp. 441-448. INSTICC.
Eduardo Luz, Pedro Silva, Rodrigo Silva, Ludmila Silva, João Guimarães, Gustavo Miozzo, Gladston Moreira, and David Menotti. 2022. “Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images.” Research on Biomedical Engineering, 38, 1, Pp. 149-162. Publisher's VersionAbstract
Confronting the pandemic of COVID-19 is nowadays one of the most prominent challenges of the human species. A key factor in slowing down the virus propagation is the rapid diagnosis and isolation of infected patients. The standard method for COVID-19 identification, the Reverse transcription polymerase chain reaction method, is time-consuming and in short supply due to the pandemic. Thus, researchers have been looking for alternative screening methods, and deep learning applied to chest X-rays of patients has been showing promising results. Despite their success, the computational cost of these methods remains high, which imposes difficulties to their accessibility and availability. Thus, the main goal of this work is to propose an accurate yet efficient method in terms of memory and processing time for the problem of COVID-19 screening in chest X-rays.