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Statistics in Human Genetics and Molecular Biology

Cavan Reilly
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advanced statistical genomics techniques
Alignment Score
Allele IBD
Author_Cavan Reilly
Backward Recursion
bioinformatics
biostatistics methods
Category=PSAK
Category=PSD
Category=PSX
Cavan Reilly
cluster analysis
DE
Diagonal Linear Discriminant Analysis
distribution
DNA
DNA Molecule
Dot Plot
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
expression
Extragenic DNA
Forward Recursion
fraction
Gap Penalty
gene expression profiling
genetics
genome mapping
genomic data analysis
graduate level genetics
Hemoglobin Subunit Alpha
hidden
Hidden Markov Models
LOD Score
markov
Markov Chain
micro arrays
models
molecular biology
MSI Status
Multiple Alignment
nonparametric association analysis
posterior
Posterior Distribution
Posterior Mode
Probe Level Data
Radiation Hybrid
Radiation Hybrid Panel
random
recombination
Recombination Fraction
Roc Curve
sequence feature discovery
Transcription Start Site
Treat Parameter Estimation
variable

Product details

  • ISBN 9781420072631
  • Weight: 690g
  • Dimensions: 156 x 234mm
  • Publication Date: 19 Jun 2009
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

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.
About Cavan Reilly

Cavan Reilly is associate professor of biostatistics at the University of Minnesota.

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