DNA microarray is a multiplex technology used in molecular biology
and in medicine. They provide a powerful means for probing the functional
states of a cell population by allowing simultaneous observation
of mRNA expression patterns of all their genes collected over time
and/or under different experimental conditions. DNA microarrays
can be used to measure changes in expression levels, so that one
can possibly derive information about genes associated with a particular
cellular condition even specific biochemical pathways.
way to visualize microarray data for gene expression analyses is
to represent the dataset as a matrix with rows representing the
genes and columns representing the conditions (or the other way
around) with each element of the matrix representing the relative
mRNA abundance of a gene under a specific condition.
the complex microarray data, numerous computational tools have been
developed. Among them, clustering of genes based on their similar
expression patterns (co-expressed genes) using (traditional) clustering
strategies represents one of the most popular approaches to microarray
traditional clustering techniques attempt to, in the context of
microarray data analyses, partition a set of genes into "clusters"
with similar expression patterns under specified conditions, or
identify such clusters from an otherwise unstructured microarray
dataset. While useful, such clustering algorithms are known to be
inadequate for handling the general gene-expression analyses problems,
that often need to identify co-expressed genes under some (to-be-identified)
conditions in contrast to finding co-expressed genes under all given
Biclustering (co-clustering) is a data mining technique which allows
simultaneous clustering of the rows and columns of a matrix. It
is Cheng and Church who firstly introduced the concept of direct
clustering, originally proposed by Hartigan, to the field of gene
expression data analyses, and referred it as biclustering which
extends the traditional clustering techniques, that is to find subsets
of conditions under which some (to be identified) subsets of genes
have similar expression patterns. Each such submatrix is called
algorithms have been developed to attempt to solve the biclustering
problem, such as BIMAX, ISA, SAMBA, RMSBE, BOLBOA, NNN and BUBBLE.
We recently developed a highly effective biclustering algorithm,
QUalitative BIClustering algorithm (QUBIC) (1).
This server, QServer, has employed the QUBIC algorithm and a number
of functional characterizations of each bicluster of genes. We believe
that QServer will be an easy-to-use and hypothesis-intriguing platform
for genomics researchers.
 G Li, Q Ma, H Tang, AH Paterson and Y Xu,
"QUBIC: a qualitative biclustering algorithm for analyses of
gene expression data", Nucleic Acids Research 2009