Rui Camacho

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.

Abstract

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.

Abstract
The exponential growth of documents in the web makes it very hard for researchers to be aware of the relevant work being done within the scientific community. The task of efficiently retrieving information has therefore become an important research topic. The objective of this study is to test how the efficiency of the text classification changes if different weights are previously assigned to the sections that compose the documents. The proposal takes into account the place (section) where terms are located in the document, and each section has a weight that can be modified depending on the corpus. To carry out the study, an extended version of the OHSUMED corpus with full documents have been created. Through the use of WEKA, we compared the use of abstracts only with that of full texts, as well as the use of section weighing combinations to assess their significance in the scientific article classification process using the SMO (Sequential Minimal Optimization), the WEKA Support Vector Machine (SVM) algorithm implementation. The experimental results show that the proposed combinations of the preprocessing techniques and feature selection achieve promising results for the task of full text scientific document classification. We also have evidence to conclude that enriched datasets with text from certain sections achieve better results than using only titles and abstracts.

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.