By Dipak K. Dey,Samiran Ghosh,Bani K. Mallick
Bayesian Modeling in Bioinformatics discusses the advance and alertness of Bayesian statistical equipment for the research of high-throughput bioinformatics info bobbing up from difficulties in molecular and structural biology and disease-related clinical study, comparable to melanoma. It offers a large review of statistical inference, clustering, and type difficulties in major high-throughput structures: microarray gene expression and phylogenic analysis.
The e-book explores Bayesian options and types for detecting differentially expressed genes, classifying differential gene expression, and deciding on biomarkers. It develops novel Bayesian nonparametric methods for bioinformatics difficulties, dimension mistakes and survival types for cDNA microarrays, a Bayesian hidden Markov modeling method for CGH array info, Bayesian methods for phylogenic research, sparsity priors for protein-protein interplay predictions, and Bayesian networks for gene expression information. The textual content additionally describes functions of mode-oriented stochastic seek algorithms, in vitro to in vivo issue profiling, proportional dangers regression utilizing Bayesian kernel machines, and QTL mapping.
Focusing on layout, statistical inference, and information research from a Bayesian viewpoint, this quantity explores statistical demanding situations in bioinformatics facts research and modeling and gives suggestions to those difficulties. It encourages readers to attract at the evolving applied sciences and advertise statistical improvement during this quarter of bioinformatics.
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Additional resources for Bayesian Modeling in Bioinformatics (Chapman & Hall/CRC Biostatistics Series)
Bayesian Modeling in Bioinformatics (Chapman & Hall/CRC Biostatistics Series) by Dipak K. Dey,Samiran Ghosh,Bani K. Mallick