Selecting maximally informative genes

Exploring Classifiability Metrics for Selecting Informative Genes

Microarray experiments are emerging as one of the main driving forces in modern biology. By allowing the simultaneous monitoring of the expression of the entire genome for a given organism, array experiments provide tremendous insight into the fundamental biological processes that translate genetic information. One of the major challenges is to identify computationally efficient and biologicall...

Direct integration of microarrays for selecting informative genes and phenotype classification

The ability to provide thousands of gene expression values simultaneously makes microarray data very useful for phenotype classification. A major constraint in phenotype classification is that the number of genes greatly exceeds the number of samples. We overcame this constraint in two ways; we increased the number of samples by integrating independently generated microarrays that had been desi...

Identification of Alzheimer disease-relevant genes using a novel hybrid method

Identifying genes underlying complex diseases/traits that generally involve multiple etiological mechanisms and contributing genes is difficult. Although microarray technology has enabled researchers to investigate gene expression changes, but identifying pathobiologically relevant genes remains a challenge. To address this challenge, we apply a new method for selecting the disease-relevant gen...

Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium.

Common genetic polymorphisms may explain a portion of the heritable risk for common diseases. Within candidate genes, the number of common polymorphisms is finite, but direct assay of all existing common polymorphism is inefficient, because genotypes at many of these sites are strongly correlated. Thus, it is not necessary to assay all common variants if the patterns of allelic association betw...

Maximally Informative Statisti s 3 dimensional