DeBi: Discovering Differentially Expressed Biclusters using a Frequent Itemset Approach.

ABSTRACT:

Background

The analysis of massive high throughput data via clustering algorithms is very important for elucidating gene functions in biological systems. However, traditional clustering methods have several drawbacks. Biclustering overcomes these limitations by grouping genes and samples simultaneously. It discovers subsets of genes that are co-expressed in certain samples. Recent studies showed that biclustering has a great potential in detecting marker genes that are associated with certain tissues or diseases. Several biclustering algorithms have been proposed. However, it is still a challenge to find biclusters that are significant based on biological validation measures. Besides that, there is a need for a biclustering algorithm that is capable of analyzing very large datasets

SUBMITTER: Serin A 

PROVIDER: S-EPMC3152888 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature