Details
Name
Rui CamachoCluster
Computer ScienceRole
Senior ResearcherSince
01st January 2011
Nationality
PortugalCentre
Artificial Intelligence and Decision SupportContacts
+351220402963
rui.camacho@inesctec.pt
2021
Authors
Domingues, MAP; Camacho, R; Rodrigues, PP;
Publication
Journal of Biomedical Informatics
Abstract
Over the last decades clinical research has been driven by informatics changes nourished by distinct research endeavors. Inherent to this evolution, several issues have been the focus of a variety of studies: multi-location patient data access, interoperability between terminological and classification systems and clinical practice and records harmonization. Having these problems in mind, the Data Safe Haven paradigm emerged to promote a newborn architecture, better reasoning and safe and easy access to distinct Clinical Data Repositories. This study aim is to present a novel solution for clinical search harmonization within a safe environment, making use of a hybrid coding taxonomy that enables researchers to collect information from multiple repositories based on a clinical domain query definition. Results show that is possible to query multiple repositories using a single query definition based on clinical domains and the capabilities of the Unified Medical Language System, although it leads to deterioration of the framework response times. Participants of a Focus Group and a System Usability Scale questionnaire rated the framework with a median value of 72.5, indicating the hybrid coding taxonomy could be enriched with additional metadata to further improve the refinement of the results and enable the possibility of using this system as data quality tagging mechanism. © 2020 Elsevier Inc.
2021
Authors
Oliveira, M; Oliveira, J; Camacho, R; Ferreira, C;
Publication
Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies
Abstract
2021
Authors
Cavadas, B; Leite, M; Pedro, N; Magalhaes, AC; Melo, J; Correia, M; Maximo, V; Camacho, R; Fonseca, NA; Figueiredo, C; Pereira, L;
Publication
Microorganisms
Abstract
The continuous characterization of genome-wide diversity in population and case- cohort samples, allied to the development of new algorithms, are shedding light on host ancestry impact and selection events on various infectious diseases. Especially interesting are the longstanding associations between humans and certain bacteria, such as the case of Helicobacter pylori, which could have been strong drivers of adaptation leading to coevolution. Some evidence on admixed gastric cancer cohorts have been suggested as supporting Homo-Helicobacter coevolution, but reliable experimental data that control both the bacterium and the host ancestries are lacking. Here, we conducted the first in vitro coinfection assays with dual humanand bacterium-matched and -mismatched ancestries, in African and European backgrounds, to evaluate the genome wide gene expression host response to H. pylori. Our results showed that: (1) the host response to H. pylori infection was greatly shaped by the human ancestry, with variability on innate immune system and metabolism; (2) African human ancestry showed signs of coevolution with H. pylori while European ancestry appeared to be maladapted; and (3) mismatched ancestry did not seem to be an important differentiator of gene expression at the initial stages of infection as assayed here. © 2021 by the authors.
2021
Authors
Goncalves, CAO; Camacho, R; Goncalves, CT; Vieira, AS; Diz, LB; Iglesias, EL;
Publication
Applied Sciences
Abstract
2021
Authors
Ferreira, P; Ladeiras, J; Camacho, R;
Publication
Practical Applications of Computational Biology & Bioinformatics, 15th International Conference (PACBB 2021), Salamanca, Spain, 6-8 October, 2021.
Abstract
Cancer is one of the diseases with the highest mortality rate in the world. To understand the different origins of the disease, and to facilitate the development of new ways to treat it, laboratories cultivate, in vitro, cancer cells (cell lines), taken from patients with cancer. These cell lines enable researchers to test new approaches and to have an appropriate procedure for comparison of results. The methods used in an initial study at EMBL-EBI Institute (Cambridge, UK) were based on algorithms that construct “propositional like” models. The results reported were promising but we believe that they can be improved. A relevant limitation of the algorithms used in the original study is the absence or severe lack of comprehensibility of the models constructed. In Life Sciences, the possibility of understanding a model is an asset to help the specialist to understand the phenomenon that produced the data. With our study we have improved the performance of forecasting models and constructed understandable models. To meet these objectives we have used Graph Mining and Inductive Logic Programming algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Supervised Thesis
2021
Author
Paulo Sérgio Vieira da Costa
Institution
UP-FEUP
2021
Author
Pedro Miguel Santos Ferreira
Institution
UP-FEUP
2021
Author
Luís Ricardo Marques Oliveira
Institution
UP-FEUP
2021
Author
Mafalda Falcão Torres Veiga de Ferreira
Institution
UP-FEUP
2021
Author
Pedro Manuel Correia de Abreu
Institution
UP-FEUP
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