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Computational biology is an interdisciplinary research that applies approaches and methodologies of information sciences and engineering to address complex problems 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 issues and applications of computational biology in oncology. This book contains 11 chapters that cover diverse advanced computational methods applied to oncology in an attempt to find 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 biclustering 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 microarray data analysis, the work by Lˆe Cao and McLachlan in Chap. 3 discusses the difficulties encountered when dealing with microarray data subjected to selection bias, multiclass, and unbalanced problems, which can be overcome by careful selection of gene expression profiles. Novel methods presented in these chapters can be applied for developing diagnostic tests and therapeutic treatments for cancer patients.
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