Iterative signature algorithm (ISA) is one of the earliest algorithm and widely used. It was available in bioconductor (‘eisa’ package) early on. But now there are many biclustering algorithms available. For example, bimax, plaid, SAMBA etc. You can check this link:
and look up “bicluster” to see the other biclustering algorithms available in bioconductor. Some other packages such as ‘Biclust’ is available as R package. The nice feature of Biclust is that it we can run five different biclustering algorithms and compare.
I think, with the availability of too many biclustering algorithm, comparing and choosing a relevant one is a problem. Visualization of resulting biclusters and extracting meaningful information out of them seems to be an active area research in this field.
One advantage of biclustering is that same genes/conditions can be present in multiple clusters. It also gives a problem that we get many overlapping clusters. Then we have to choose whether it is meaningful to treat these clusters separately or to merge them.
You can check this paper which does a comparison of available biclusters tools:
This paper shows a nice application of biclustering, i.e, finding new disease-relevant genes by mining large gene expression data.
Making a new tool available in bioconductor and publishing a clear biological application will lead to rapid usage by the community. ISA algorithm did that with their early papers: http://bioinformatics.oxfordjournals.org/content/20/13/1993.long
thanks! Didn't expect an answer that clear and informative!