Recognizing complicated biclusters submerged in large scale datasets (matrix) has been being a highly challenging problem. We introduce a biclustering algorithm BicGO consisting of two separate strategies which can be selectively used by users. The BicGO which was developed based on global optimization can be implemented by iteratively answering if a real number belongs to a given interval. Tested on various simulated datasets in which most complicated and most general trend-preserved biclusters were submerged, BicGO almost always extracted all the actual bicluters with accuracy close to 100%, while on real datasets, it also achieved an incredible superiority over all the salient tools compared in this article. As far as we know, the BicGO is the first tool capable of identifying any complicated (e.g., constant, shift, scale, shiftscale, order-preserved, trend-preserved, etc), any shapes (narrow or broad) of biclusters with overlaps allowed. In addition, it is also highly parsimonious in the usage of computing resources. The BicGO is available at https://www.dropbox.com/s/hsj3j96rekoks5n/BicGO.zip?dl=0 for free download.
Li and Su REF introduce a biclustering algorithm called BicGO to recognize complicated biclusters submerged in large scale datasets (matrix).
BicGO: a new biclustering algorithm based on global optimization
bioRxiv
Biology