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.

Will cover the entire spectrum of techniques in computational biology and their applications in the field of oncology, including microarrays, gels, mass spectra, SNPs and haplotypes, and even a brief discussion of the applications of systems biology

Identification of Relevant Genes from Microarray Experiments based on Partial Least Squares Weights: Application to Cancer Genomics.- Geometric Biclustering and Its Applications to Cancer Tissue Classification Based on DNA Microarray Gene Expression Data.- Statistical Analysis on Microarray Data: Selection of Gene Prognosis Signatures.- Agent-Based Modeling of Ductal Carcinoma In Situ: Application to Patient-Specific Breast Cancer Modeling.- Multicluster Class-Based Classification for the Diagnosis of Suspicious Areas in Digital Mammograms.- Analysis of Cancer Data Using Evolutionary Computation.- Analysis of Population-Based Genetic Association Studies Applied to Cancer Susceptibility and Prognosis.- Selected Applications of Graph-Based Tracking Methods for Cancer Research.- Recent Advances in Cell Classification for Cancer Research and Drug Discovery.- Computational Tools and Resources for Systems Biology Approaches in Cancer.- Laser Speckle Imaging for Blood Flow Analysis.- The Challenges in Blood Proteomic Biomarker Discovery.