Keywords:
Human genetics -- Statistical methods.
;
Genomics -- Statistical methods.
;
Molecular biology -- Statistical methods.
;
Electronic books.
Description / Table of Contents:
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.
Type of Medium:
Online Resource
Pages:
1 online resource (284 pages)
Edition:
1st ed.
ISBN:
9781420072648
Series Statement:
Chapman and Hall/CRC Texts in Statistical Science Series
URL:
https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=1579771
DDC:
572.8072/7
Language:
English
Note:
Cover -- Title -- Copyright -- Contents -- Preface -- CHAPTER 1: Basic Molecular Biology for Statistical Genetics and Genomics -- CHAPTER 2: Basics of Likelihood Based Statistics -- CHAPTER 3: Markers and Physical Mapping -- CHAPTER 4: Basic Linkage Analysis -- CHAPTER 5: Extensions of the Basic Model for Parametric Linkage -- CHAPTER 6: Nonparametric Linkage and Association -- CHAPTER 7: Sequence Alignment -- CHAPTER 8: Significance of Alignments and Alignment in Practice -- CHAPTER 9: Hidden Markov Models -- CHAPTER 10: Feature Recognition in Biopolymers -- CHAPTER 11: Multiple Alignment and Sequence Feature Discovery -- CHAPTER 12: Statistical Genomics -- CHAPTER 13: Detecting Differential Expression -- CHAPTER 14: Cluster Analysis in Genomics -- CHAPTER 15: Classification in Genomics -- References -- Index.
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