CaBIG VISDA: modeling, visualization, and discovery for cluster analysis of genomic data.

ABSTRACT:

Background

The main limitations of most existing clustering methods used in genomic data analysis include heuristic or random algorithm initialization, the potential of finding poor local optima, the lack of cluster number detection, an inability to incorporate prior/expert knowledge, black-box and non-adaptive designs, in addition to the curse of dimensionality and the discernment of uninformative, uninteresting cluster structure associated with confounding variables.

Results

In an effort to partially address these limitations, we develop the VIsual Statistical Data Analyzer (VISDA) for cluster modeling, visualization, and discovery in genomic data. VISDA performs progressive, coarse-to-fine (divisive) hierarchical clustering and visualization, supported by hierarchical mix

SUBMITTER: Zhu Y 

PROVIDER: S-EPMC2566986 | biostudies-literature | 2008 Sep

REPOSITORIES: biostudies-literature