Recognizing complicated biclusters submerged in large scale datasets (matrix) has been being a highly challenging problem. We introduce a Biclustering algorithm BicGO that is able to identify any complicated biclusters no matter narrow or broad. The BicGO consists of two separate strategies, i.e., column-based and row-based, that can be selectively used by users, which were developed based on Global Optimization. The column-based can be implemented by iteratively answering if a given real number belongs to a given interval, and the row-based by iteratively finding a longest path in a directed acyclic graph. Tested on various simulated datasets in which most complicated and meaningful trend-preserved biclusters were submergingly implanted, BicGO always precisely extracted all the implanted bicluters, with accuracy 100% unless either too much noise or overlap involved. On real datasets, it also achieved an incredible superiority over all the salient tools compared in this article. It is the first tool capable of identifying any complicated biclusters of any shapes. In addition, it is also highly parsimonious in the usage of computing resources.

I received my PhD degree from Institute of Mathematics and Systems Science, Chinese Academy of Science, Beijing, China in 1996, and moved to Shandong University, China as a full professor. I was hired as Senior Research Scientist by University of Georgia, USA since 2004. I have a broad background in mathematics, with specific training and expertise in graph theory and combinatorial optimization, especially in development of algorithm for dealing with computationally challenging problems from biology. My research in the related areas of mathematics, computer science and bioinformatics has appeared in over 100 papers in refereed journals. My research is notable for having settled several fundamental problems in computer science and discrete mathematics. Since 2004 my research has focused on bioinformatics. I have developed algorithms and software packages for solving a number of challenging computational biology problems. My varied research experience has honed my skills in mathematical modeling, algorithm design, software development, and collaboration with scientists from different research areas.