AUC scores of predictive models fit with varying <i>α</i>, for <i>α</i> ∈ {0.01, 0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95, 0.99}.

<p>AUC scores of predictive models fit with varying <i>α</i>, for <i>α</i> ∈ {0.01, 0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95, 0.99}.</p

AUC scores of univariate predictors included in the BRCA and GBM models, in comparison with the AUC scores of the models themselves.

<p>(a) shows BRCA, (b) shows GBM.</p

CDF curves of prediction scores from stepped-down logistic regression models for each data set.

<p>(a) shows BRCA, (b) shows GBM.</p

The workflow of the proposed algorithmic pipeline that integrates mutation, gene expression, and protein-protein interaction (PPI) data to test the driving hypothesis and identify causal genes.

<p>The workflow of the proposed algorithmic pipeline that integrates mutation, gene expression, and protein-protein interaction (PPI) data to test the driving hypothesis and identify causal genes.</p

Curso: introdução à gestão de processos

O curso faz parte da temática de Gestão de Processos e constitui, na trilha de cursos proposta para a temática, um curso introdutório sobre o assunto. Apresenta conceitos básicos sobre o tema gestão de processos e oferece uma ótima oportunidade de aprendizagem àqueles interessados em aprimorar a sua atuação profissional, através da compreensão de conceitos relacionados à Gestão de Processos aplicados à organização. Para isso são apresentadas técnicas para identificar, mapear, redesenhar, melhorar e gerir processos de trabalho que auxiliam a tomada de decisão, melhorando de forma contínua o desempenho organizacional.4 módulos em um arquivo .zipGestão de ProcessosMódulo 1: Introdução e Conceitos Básicos - http://repositorio.enap.gov.br/handle/1/2897Módulo 2: Como gerir e melhorar Processos - http://repositorio.enap.gov.br/handle/1/2898Módulo 3: Definir e Planejar Indicadores Estratégicos - http://repositorio.enap.gov.br/handle/1/2899Módulo 4: ferramentas para gestão de processos - http://repositorio.enap.gov.br/handle/1/290

Prediction scores of highest-scoring genes that are not contained in respective pathways: BRCA in (a) and GBM in (b).

<p>Prediction scores of highest-scoring genes that are not contained in respective pathways: BRCA in (a) and GBM in (b).</p

Log-rank <i>P</i>-values of differences in patient outcome (survival), using top-scoring genes that are not present each disease’s respective pathway.

<p>BRCA is shown in (a); GBM is shown in (b). For each gene, distinct tests are performed using mutation and differential expression status to separate the samples into two groups. −log₁₀(log-rank <i>P</i>-value) is plotted on the <i>y</i>-axis. The horizontal grey line denotes the 0.05 <i>P</i>-value cutoff.</p

Additional file 3: of GLADIATOR: a global approach for elucidating disease modules

GLADIATOR code. (PY 16 kb

CDF curves for individual features included in the prediction model.

<p><i>P</i>-values show Kolmogorov-Smirnov test results. Genes are separated by pathway membership; (a) shows BRCA, (b) shows GBM.</p

Areas under the receiver-operating-characteristic curves.

<p>Areas under the curves (AUC) obtained in a 10-fold cross-validation setting. The AUC is averaged across 20 cross validation repeats.</p