Identification of disease-causing genes using microarray data mining and gene ontology

Background: One of the best and most accurate methods for identifying disease-causing genes is monitoring gene expression values in different samples using microarray technology. One of the shortcomings of microarray data is that they provide a small quantity of samples with respect to the number of genes. This problem reduces the classification accuracy of the methods, so gene selection is essential to improve the predictive accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVMRFE) has become one of the leading methods, but its performance can be reduced because of the small sample size, noisy data and the fact that the method does not remove redundant genes. Methods: We propose a novel framework for gene selection which uses the advantageous features of conventional methods and addresses their weaknesses. In fact, we have combined the Fisher method and SVMRFE to utilize the advantages of a filtering method as well as an embedded method. Furthermore, we have added a redundancy reduction stage to address the weakness of the Fisher method and SVMRFE. In addition to gene expression values, the proposed method uses Gene Ontology which is a reliable source of information on genes. The use of Gene Ontology can compensate, in part, for the limitations of microarrays, such as having a small number of samples and erroneous measurement results. Results: The proposed method has been applied to colon, Diffuse Large B-Cell Lymphoma (DLBCL) and prostate cancer datasets. The empirical results show that our method has improved classification performance in terms of accuracy, sensitivity and specificity. In addition, the study of the molecular function of selected genes strengthened the hypothesis that these genes are involved in the process of cancer growth. Conclusions: The proposed method addresses the weakness of conventional methods by adding a redundancy reduction stage and utilizing Gene Ontology information. It predicts marker genes for colon, DLBCL and prostate cancer with a high accuracy. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help in the search for a cure for cancers

GÊNEROS DISCURSIVOS E ENSINO: UMA PROPOSTA DE APLICAÇÃO EM SALA DE AULA

Os gêneros discursivos são formas de agir e interagir discursivamente e são inerentes à comunicação humana. Neste artigo, nos propomos, a partir de um percurso teórico, discutir sobre o conceito de gênero discursivo com base nas reflexões de Bakhtin (2000) e Marcuschi (2003, 2005), considerando sua aplicabilidade no ensino como condição para assegurar à construção de conhecimentos fundamentais para as práticas sociais de linguagem. Para isso, refletimos sobre o gênero discursivo como atividade sociocomunicativa de interação social, produzido para as necessidades de comunicação, constituído de componentes sociais, históricos, culturais e cognitivos. Além disso, analisamos a sequência didática na perspectiva de Dolz e Schneuwly (2004) como possibilidade de auxiliar o ensino através dos gêneros.  Entendemos ser essencial, por essa razão, que as aulas de língua portuguesa centrem-se, nos diferentes níveis de ensino, nas dinâmicas sociais de interação por meio dos gêneros discursivos.       &nbsp

Plasma lipidomic profiles improve upon traditional risk factors for the prediction of cardiovascular events in type 2 diabetes

Background- Clinical lipid measurements do not show the full complexity of the altered lipid metabolism associated with diabetes or cardiovascular disease. Lipidomics enables the assessment of hundreds of lipid species as potential markers for disease risk. Methods- Plasma lipid species (310) were measured by a targeted lipidomic analysis with liquid chromatography electrospray ionisation-tandem mass spectrometry on a case-cohort (n=3,779) subset from the ADVANCE (Action in Diabetes and Vascular disease: preterAx and diamicroN-MR Controlled Evaluation) trial. The-case cohort was 61% male with a mean age of 67. All participants had type 2 diabetes mellitus with one or more additional cardiovascular risk factors and 35% had a history of macrovascular disease. Weighted Cox regression was used to identify lipid species associated with future cardiovascular events (non-fatal myocardial infarction, non-fatal stroke and cardiovascular death) and cardiovascular death during a five year follow-up period. Multivariable models combining traditional risk factors with lipid species were optimized using the Akaike information criteria. C-statistics and net reclassification indices (NRI) were calculated within a five-fold cross validation framework. Results- Sphingolipids, phospholipids (including lyso- and ether- species), cholesteryl esters and glycerolipids were associated with future cardiovascular events and cardiovascular death. The addition of 7 lipid species to a base model (14 traditional risk factors and medications) to predict cardiovascular events increased the C-statistic from 0.680 (95% CI, 0.678-0.682) to 0.700 (95% CI, 0.698-0.702, p&lt;0.0001) with a corresponding continuous NRI of 0.227 (95% CI, 0.219-0.235). The prediction of cardiovascular death was improved with the incorporation of 4 lipid species to the base model, showing an increase in the C-statistic from 0.740 (95% CI, 0.738-0.742) to 0.760 (95% CI, 0.757-0.762, p&lt;0.0001) and a continuous NRI of 0.328 (95%CI, 0.317-0.339). The results were validated in a subcohort with type 2 diabetes (n=511) from the LIPID (The Long-Term Intervention with Pravastatin in Ischaemic Disease) trial. Conclusion- The improvement in the prediction of cardiovascular events, above traditional risk factors, demonstrates the potential of plasma lipid species as biomarkers for cardiovascular risk stratification in diabetes.</p

Plasma lipidomic profiles improve on traditional risk factors for the prediction of cardiovascular events in type 2 diabetes mellitus

Background: Clinical lipid measurements do not show the full complexity of the altered lipid metabolism associated with diabetes mellitus or cardiovascular disease. Lipidomics enables the assessment of hundreds of lipid species as potential markers for disease risk. Methods: Plasma lipid species (310) were measured by a targeted lipidomic analysis with liquid chromatography electrospray ionization–tandem mass spectrometry on a case-cohort (n=3779) subset from the ADVANCE trial (Action in Diabetes and Vascular Disease: Preterax and Diamicron-MR Controlled Evaluation). The case-cohort was 61% male with a mean age of 67 years. All participants had type 2 diabetes mellitus with ≥1 additional cardiovascular risk factors, and 35% had a history of macrovascular disease. Weighted Cox regression was used to identify lipid species associated with future cardiovascular events (nonfatal myocardial infarction, nonfatal stroke, and cardiovascular death) and cardiovascular death during a 5-year follow-up period. Multivariable models combining traditional risk factors with lipid species were optimized with the Akaike information criteria. C statistics and NRIs were calculated within a 5-fold cross-validation framework. Results: Sphingolipids, phospholipids (including lyso- and ether- species), cholesteryl esters, and glycerolipids were associated with future cardiovascular events and cardiovascular death. The addition of 7 lipid species to a base model (14 traditional risk factors and medications) to predict cardiovascular events increased the C statistic from 0.680 (95% confidence interval [CI], 0.678–0.682) to 0.700 (95% CI, 0.698–0.702; P<0.0001) with a corresponding continuous NRI of 0.227 (95% CI, 0.219–0.235). The prediction of cardiovascular death was improved with the incorporation of 4 lipid species into the base model, showing an increase in the C statistic from 0.740 (95% CI, 0.738–0.742) to 0.760 (95% CI, 0.757–0.762; P<0.0001) and a continuous net reclassification index of 0.328 (95% CI, 0.317–0.339). The results were validated in a subcohort with type 2 diabetes mellitus (n=511) from the LIPID trial (Long-Term Intervention With Pravastatin in Ischemic Disease). Conclusions: The improvement in the prediction of cardiovascular events, above traditional risk factors, demonstrates the potential of plasma lipid species as biomarkers for cardiovascular risk stratification in diabetes mellitus

Establishing multiple omics baselines for three Southeast Asian populations in the Singapore Integrative Omics Study

The Singapore Integrative Omics Study provides valuable insights on establishing population reference measurement in 364 Chinese, Malay, and Indian individuals. These measurements include > 2.5 millions genetic variants, 21,649 transcripts expression, 282 lipid species quantification, and 284 clinical, lifestyle, and dietary variables. This concept paper introduces the depth of the data resource, and investigates the extent of ethnic variation at these omics and non-omics biomarkers. It is evident that there are specific biomarkers in each of these platforms to differentiate between the ethnicities, and intra-population analyses suggest that Chinese and Indians are the most biologically homogeneous and heterogeneous, respectively, of the three groups. Consistent patterns of correlations between lipid species also suggest the possibility of lipid tagging to simplify future lipidomics assays. The Singapore Integrative Omics Study is expected to allow the characterization of intra-omic and inter-omic correlations within and across all three ethnic groups through a systems biology approach.The Singapore Genome Variation projects characterized the genetics of Singapore's Chinese, Malay, and Indian populations. The Singapore Integrative Omics Study introduced here goes further in providing multi-omic measurements in individuals from these populations, including genetic, transcriptome, lipidome, and lifestyle data, and will facilitate the study of common diseases in Asian communities