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Cancer Gene Profiling

Overview of attention for book
Cover of 'Cancer Gene Profiling'

Table of Contents

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    Book Overview
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    Chapter 1 Factors Affecting the Use of Human Tissues in Biomedical Research: Implications in the Design and Operation of a Biorepository.
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    Chapter 2 Tissue Microdissection.
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    Chapter 3 Tissue Microarrays.
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    Chapter 4 Gene Expression Analysis in the Age of Mass Sequencing: An Introduction.
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    Chapter 5 Quantitative DNA Methylation Profiling in Cancer.
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    Chapter 6 Cancer Gene Profiling
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    Chapter 7 Exosomes in Cancer Disease.
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    Chapter 8 MicroRNA (miRNA) Profiling.
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    Chapter 9 Cancer Gene Profiling for Response Prediction.
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    Chapter 10 Target Gene Discovery for Novel Therapeutic Agents in Cancer Treatment.
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    Chapter 11 Application of Proteomics in Cancer Biomarker Discovery: GeLC-MS/MS
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    Chapter 12 Meta-analysis of Cancer Gene Profiling Data.
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    Chapter 13 Reproducible, Scalable Fusion Gene Detection from RNA-Seq.
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    Chapter 14 Transcriptome Sequencing for the Detection of Chimeric Transcripts.
Attention for Chapter 6: Cancer Gene Profiling
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Citations

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Chapter title
Cancer Gene Profiling
Chapter number 6
Book title
Cancer Gene Profiling
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3204-7_6
Pubmed ID
26667457
Book ISBNs
978-1-4939-3203-0, 978-1-4939-3204-7
Authors

Shaw, Victoria, Bullock, Katie, Greenhalf, William, Victoria Shaw, Katie Bullock, William Greenhalf

Editors

Robert Grützmann, Christian Pilarsky

Abstract

Genetic heterogeneity explains variation in predisposition for cancer. Whole-genome analysis allows risk to be quantified, giving better targeted screening and quantification of the personalized risk posed by environmental factors. Array-based approaches to whole-genome analysis are rapidly being overtaken by next-generation sequencing (NGS). In this review the different platforms currently available for NGS are compared and the opportunities and risks of this approach are discussed: including the informatics packages required and the ethical issues. Methods applicable to the personal genome machine (PGM) are given as an example of workflows.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 20%
Student > Ph. D. Student 2 20%
Student > Bachelor 1 10%
Other 1 10%
Professor 1 10%
Other 0 0%
Unknown 3 30%
Readers by discipline Count As %
Medicine and Dentistry 3 30%
Biochemistry, Genetics and Molecular Biology 2 20%
Computer Science 1 10%
Unknown 4 40%

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