Prediction of disease genes using tissue-specified gene-gene network

Network-basedmethods for human disease gene prediction

Despite the considerable progress in disease gene discovery, we are far from uncovering the underlying cellular mechanisms of diseases since complex traits, even many Mendelian diseases, cannot be explained by simple genotype^phenotype relationships. More recently, an increasingly accepted view is that human diseases result from perturbations of cellular systems, especially molecular networks. ...

Cancer Gene Prediction Using a Network Approach

Prediction of blood cancer using leukemia gene expression data and sparsity-based gene selection methods

Background: DNA microarray is a useful technology that simultaneously assesses the expression of thousands of genes. It can be utilized for the detection of cancer types and cancer biomarkers. This study aimed to predict blood cancer using leukemia gene expression data and a robust ℓ2,p-norm sparsity-based gene selection method. Materials and Methods: In this descriptive study, the microarray ...

assessment of the efficiency of s.p.g.c refineries using network dea

data envelopment analysis (dea) is a powerful tool for measuring relative efficiency of organizational units referred to as decision making units (dmus). in most cases dmus have network structures with internal linking activities. traditional dea models, however, consider dmus as black boxes with no regard to their linking activities and therefore do not provide decision makers with the reasons...

Bioinformatic analysis of autism positional candidate genes using biological databases and computational gene network prediction.

Common genetic disorders are believed to arise from the combined effects of multiple inherited genetic variants acting in concert with environmental factors, such that any given DNA sequence variant may have only a marginal effect on disease outcome. As a consequence, the correlation between disease status and any given DNA marker allele in a genomewide linkage study tends to be relatively weak...