A Comparative Analysis of Biclustering Algorithms for Gene Expression Data

The need to analyze high-dimension biological data is driv-ing the development of new data mining methods. Biclus-tering algorithms have been successfully applied to gene ex-pression data to discover local patterns, in which a subset of genes exhibit similar expression levels over a subset of con-ditions. However, it is not clear which algorithms are best suited for this task. Many algorithms have been published in the past decade, most of which have been compared only to a small number of algorithms. Surveys and comparisons exist in the literature, but because of the large number and variety of biclustering algorithms, they are quickly outdated. In this paper we partially address this problem of evalu-ating the strengths and weaknesses of existing biclustering methods. We used the BiBench package to compare twelv