Background: Principal component analysis (PCA) has gained popularity as a method for the analysis of highdimensional genomic data. However, it is often difficult to interpret the ...
Background: Modern high throughput experimental techniques such as DNA microarrays often result in large lists of genes. Computational biology tools such as clustering are then us...
Alain B. Tchagang, Alexander Gawronski, Hugo B&eac...
Background: The analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature sy...
Javier Garcia-Garcia, Emre Guney, Ramon Aragues, J...
Background: The estimation of demographic parameters from genetic data often requires the computation of likelihoods. However, the likelihood function is computationally intractab...
Daniel Wegmann, Christoph Leuenberger, Samuel Neue...
Background: Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the under...