Browsing by Issue Date, starting with "2018-12-10"
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- A Farmacogenómica em Farmacovigilância Projeto RAM-PredictPublication . Moura Vicente, AstridFarmacogenómica – Estudo da contribuição dos genes para a variabilidade na resposta individual a medicamentos. A medicina personalizada é amplamente entendida como um modelo médico que utiliza a caracterização dos fenótipos e genótipos das pessoas (por exemplo, a caracterização molecular, a imagiologia médica, dados relativos ao estilo de vida) para ajustar a estratégia terapêutica a cada pessoa no momento certo, e/ou para determinar a predisposição a doenças e/ou para prestar cuidados preventivos atempados e devidamente direcionados. (Conclusões do Conselho da União Europeia sobre a medicina personalizada para os doentes, 2015/C 421/03)
- Optimization of the harvesting and freezing conditions of human cell lines for DNA damage analysis by the alkaline Comet assayPublication . Bessa, Maria João; Brandão, Fátima; Machado Querido, Micaela; Costa Pereira, Cristiana; Valdiglesias, Vanessa; Laffon, Blanca; Carriere, Marie; Teixeira, João Paulo; Fraga, SóniaThe comet assay is a commonly used method for in vitro and in vivo genotoxicity assessment. This versatile assay can be performed in a wide range of tissues and cell types. Although most of the studies use samples immediately processed after collection, frozen biological samples can also be used. The present study aimed to optimize a collection and freezing protocol to minimize the DNA damage associated with these procedures in human cell line samples for comet assay analysis. This study was conducted in glial A172 and lung alveolar epithelial A549 cells. Two cell detachment methods (mechanical vs enzymatic) and two cryoprotective media [FBS + 10% DMSO vs Cell Culture Media (CCM) + 10% DMSO] were tested, and DNA damage assessed at four time points following storage at −80 °C (one, two, four and eight weeks). In both cell lines, no differences in % tail intensity were detected between fresh and frozen cells up to eight weeks, irrespective of the harvesting method and freezing medium used. However, freshly isolated A172 cells exhibited a significant lower DNA damage when resuspended in CCM + 10% DMSO, while for A549 fresh cells the preferable harvesting method was the enzymatic one since it induced less DNA damage. Although both harvesting methods and cryoprotective media tested were found suitable, our data indicate that enzymatic harvesting and cryopreservation in CCM + 10% DMSO is a preferable method for DNA integrity preservation of human cell line samples for comet assay analysis. Our data also suggest that CCM is a preferable and cost-effective alternative to FBS in cryopreservation media. This optimized protocol allows the analysis of in vitro cell samples collected and frozen at different locations, with minimal interference on the basal DNA strand break levels in samples kept frozen up to eight weeks.
- Identifying disease genes using machine learning and gene functional similarities, assessed through Gene OntologyPublication . Asif, M.; Martiniano, H.F.M.C.M.; Vicente, A.M.; Couto, F.M.Identifying disease genes from a vast amount of genetic data is one of the most challenging tasks in the post-genomic era. Also, complex diseases present highly heterogeneous genotype, which difficult biological marker identification. Machine learning methods are widely used to identify these markers, but their performance is highly dependent upon the size and quality of available data. In this study, we demonstrated that machine learning classifiers trained on gene functional similarities, using Gene Ontology (GO), can improve the identification of genes involved in complex diseases. For this purpose, we developed a supervised machine learning methodology to predict complex disease genes. The proposed pipeline was assessed using Autism Spectrum Disorder (ASD) candidate genes. A quantitative measure of gene functional similarities was obtained by employing different semantic similarity measures. To infer the hidden functional similarities between ASD genes, various types of machine learning classifiers were built on quantitative semantic similarity matrices of ASD and non-ASD genes. The classifiers trained and tested on ASD and non-ASD gene functional similarities outperformed previously reported ASD classifiers. For example, a Random Forest (RF) classifier achieved an AUC of 0. 80 for predicting new ASD genes, which was higher than the reported classifier (0.73). Additionally, this classifier was able to predict 73 novel ASD candidate genes that were enriched for core ASD phenotypes, such as autism and obsessive-compulsive behavior. In addition, predicted genes were also enriched for ASD co-occurring conditions, including Attention Deficit Hyperactivity Disorder (ADHD). We also developed a KNIME workflow with the proposed methodology which allows users to configure and execute it without requiring machine learning and programming skills. Machine learning is an effective and reliable technique to decipher ASD mechanism by identifying novel disease genes, but this study further demonstrated that their performance can be improved by incorporating a quantitative measure of gene functional similarities. Source code and the workflow of the proposed methodology are available at https://github.com/Muh-Asif/ASD-genes-prediction.