↓ Skip to main content

PLOS

Chapter 14: Cancer Genome Analysis

Overview of attention for article published in PLoS Computational Biology, December 2012
Altmetric Badge

Mentioned by

twitter
13 X users
facebook
1 Facebook page
wikipedia
4 Wikipedia pages

Citations

dimensions_citation
14 Dimensions

Readers on

mendeley
239 Mendeley
citeulike
11 CiteULike
Title
Chapter 14: Cancer Genome Analysis
Published in
PLoS Computational Biology, December 2012
DOI 10.1371/journal.pcbi.1002824
Pubmed ID
Authors

Miguel Vazquez, Victor de la Torre, Alfonso Valencia

Abstract

Although there is great promise in the benefits to be obtained by analyzing cancer genomes, numerous challenges hinder different stages of the process, from the problem of sample preparation and the validation of the experimental techniques, to the interpretation of the results. This chapter specifically focuses on the technical issues associated with the bioinformatics analysis of cancer genome data. The main issues addressed are the use of database and software resources, the use of analysis workflows and the presentation of clinically relevant action items. We attempt to aid new developers in the field by describing the different stages of analysis and discussing current approaches, as well as by providing practical advice on how to access and use resources, and how to implement recommendations. Real cases from cancer genome projects are used as examples.

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 239 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 5 2%
Spain 5 2%
United States 4 2%
France 2 <1%
Brazil 2 <1%
United Kingdom 2 <1%
Sweden 1 <1%
Switzerland 1 <1%
Italy 1 <1%
Other 0 0%
Unknown 216 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 73 31%
Student > Ph. D. Student 49 21%
Professor > Associate Professor 22 9%
Other 16 7%
Student > Bachelor 15 6%
Other 50 21%
Unknown 14 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 104 44%
Biochemistry, Genetics and Molecular Biology 38 16%
Medicine and Dentistry 22 9%
Computer Science 21 9%
Engineering 7 3%
Other 25 10%
Unknown 22 9%