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Cancer Driver Genes

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Cover of 'Cancer Driver Genes'

Table of Contents

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    Book Overview
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    Chapter 1 Cancer Gene Discovery: Past to Present
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    Chapter 2 Identification of Cancer Driver Genes from a Custom Set of Next Generation Sequencing Data
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    Chapter 3 Cancer Gene Discovery by Network Analysis of Somatic Mutations Using the MUFFINN Server
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    Chapter 4 Identifying Driver Interfaces Enriched for Somatic Missense Mutations in Tumors
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    Chapter 5 Identification of Cancer Genes Based on De Novo Transposon Insertion Site Analysis Using RNA and DNA Sequencing
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    Chapter 6 A Cell-Based Method for Identification of Chemotherapy Resistance Cancer Genes
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    Chapter 7 Engineering a Bioartificial Human Colon Model Through Decellularization and Recellularization
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    Chapter 8 Mutagenesis Screens for Prostate Cancer Using Replication-Incompetent Lentiviral Vectors
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    Chapter 9 Arrayed shRNA Screening to Identify Suppressors of Anchorage-Independent Growth
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    Chapter 10 Genome-Wide CRISPR/Cas9 Screening for Identification of Cancer Genes in Cell Lines
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    Chapter 11 CRISPR/Cas9-Based Positive Screens for Cancer-Related Traits
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    Chapter 12 Ex Vivo Transposon-Mediated Genetic Screens for Cancer Gene Discovery
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    Chapter 13 Cancer Gene Discovery Utilizing Sleeping Beauty Transposon Mutagenesis
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    Chapter 14 PiggyBac Transposon-Based Insertional Mutagenesis in Mice
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    Chapter 15 Liver-Specific Delivery of Sleeping Beauty Transposon System by Hydrodynamic Injection for Cancer Gene Validation
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    Chapter 16 Engineering Large Genomic Rearrangement in Mouse Embryonic Stem Cell for Cancer Gene Discovery
Attention for Chapter 11: CRISPR/Cas9-Based Positive Screens for Cancer-Related Traits
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Chapter title
CRISPR/Cas9-Based Positive Screens for Cancer-Related Traits
Chapter number 11
Book title
Cancer Driver Genes
Published by
Humana Press, New York, NY, January 2019
DOI 10.1007/978-1-4939-8967-6_11
Pubmed ID
Book ISBNs
978-1-4939-8966-9, 978-1-4939-8967-6
Authors

Nicholas J. Slipek, Jyotika Varshney, David A. Largaespada, Slipek, Nicholas J., Varshney, Jyotika, Largaespada, David A.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 11%
Researcher 1 11%
Other 1 11%
Student > Doctoral Student 1 11%
Unknown 5 56%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 22%
Unspecified 1 11%
Computer Science 1 11%
Engineering 1 11%
Unknown 4 44%