By Dey D., Ghosh S., Mallick B. (eds.)
Bayesian Modeling in Bioinformatics discusses the advance and alertness of Bayesian statistical tools for the research of high-throughput bioinformatics info coming up from difficulties in molecular and structural biology and disease-related scientific study, similar to melanoma. It provides a extensive assessment of statistical inference, clustering, and class difficulties in major high-throughput systems: microarray gene expression and phylogenic research. The ebook explores Bayesian thoughts and versions for detecting differentially expressed genes, classifying differential gene expression, and choosing biomarkers. It develops novel Bayesian nonparametric techniques for bioinformatics difficulties, size mistakes and survival versions for cDNA microarrays, a Bayesian hidden Markov modeling procedure for CGH array information, Bayesian methods for phylogenic research, sparsity priors for protein-protein interplay predictions, and Bayesian networks for gene expression info. The textual content additionally describes purposes of mode-oriented stochastic seek algorithms, in vitro to in vivo issue profiling, proportional dangers regression utilizing Bayesian kernel machines, and QTL mapping. targeting layout, statistical inference, and knowledge research from a Bayesian standpoint, this quantity explores statistical demanding situations in bioinformatics information research and modeling and gives recommendations to those difficulties. It encourages readers to attract at the evolving applied sciences and advertise statistical improvement during this region of bioinformatics.
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