Focusing on the roles of different segments of DNA, Statistics
in Human Genetics and Molecular Biology provides a basic
understanding of problems arising in the analysis of genetics and
genomics. It presents statistical applications in genetic mapping,
DNA/protein sequence alignment, and analyses of gene expression
data from microarray experiments.
The text introduces a diverse set of problems and a number of
approaches that have been used to address these problems. It
discusses basic molecular biology and likelihood-based statistics,
along with physical mapping, markers, linkage analysis, parametric
and nonparametric linkage, sequence alignment, and feature
recognition. The text illustrates the use of methods that are
widespread among researchers who analyze genomic data, such as
hidden Markov models and the extreme value distribution. It also
covers differential gene expression detection as well as
classification and cluster analysis using gene expression data
sets.
Ideal for graduate students in statistics, biostatistics,
computer science, and related fields in applied mathematics, this
text presents various approaches to help students solve problems at
the interface of these areas.
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