Computational biology is an interdisciplinary research that applies
approaches and methodologies of information sciences and engineering to
address complex pr- lems in biology. With rapid developments in the
omics and computer technologies over the past decade, computational
biology has been evolving to cover a much wider research domain and
applications in order to adequately address challenging problems in
systems biology and medicine. This edited book focuses on recent - sues
and applications of computational biology in oncology. This book
contains 11 chapters that cover diverse advanced computationalmethods
applied to oncologyin an attempt to ?nd more effective ways for the
diagnosis and cure of cancer. Chapter 1 by Chen and Nguyen addresses an
analysis of cancer genomics data using partial least squares weights for
identifying relevant genes, which are useful for follow-up validations.
In Chap. 2, Zhao and Yan report an interesting biclust- ing method for
microarray data analysis, which can handle the case when only a subset
of genes coregulates under a subset of conditions and appears to be a
novel technique for classifying cancer tissues. As another computational
method for - croarray data analysis, the work by Le ^ Cao and McLachlan
in Chap. 3 discusses the dif?culties encountered when dealing with
microarray data subjected to sel- tion bias, multiclass, and unbalanced
problems, which can be overcome by careful selection of gene expression
pro?les. Novel methods presented in these chapters can be applied for
developing diagnostic tests and therapeutic treatments for cancer
patients.
