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Clinical Bioinformatics

Overview of attention for book
Cover of 'Clinical Bioinformatics'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 From the Phenotype to the Genotype via Bioinformatics
  3. Altmetric Badge
    Chapter 2 Production and Analytic Bioinformatics for Next-Generation DNA Sequencing
  4. Altmetric Badge
    Chapter 3 Analyzing the Metabolome
  5. Altmetric Badge
    Chapter 4 Statistical Perspectives for Genome-Wide Association Studies (GWAS)
  6. Altmetric Badge
    Chapter 5 Bioinformatics Challenges in Genome-Wide Association Studies (GWAS).
  7. Altmetric Badge
    Chapter 6 Studying cancer genomics through next-generation DNA sequencing and bioinformatics.
  8. Altmetric Badge
    Chapter 7 Using Bioinformatics Tools to Study the Role of microRNA in Cancer
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    Chapter 8 Chromosome Microarrays in Diagnostic Testing: Interpreting the Genomic Data
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    Chapter 9 Bioinformatics Approach to Understanding Interacting Pathways in Neuropsychiatric Disorders
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    Chapter 10 Pathogen Genome Bioinformatics
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    Chapter 11 Setting up next-generation sequencing in the medical laboratory.
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    Chapter 12 Managing incidental findings in exome sequencing for research.
  14. Altmetric Badge
    Chapter 13 Approaches for Classifying DNA Variants Found by Sanger Sequencing in a Medical Genetics Laboratory
  15. Altmetric Badge
    Chapter 14 Designing algorithms for determining significance of DNA missense changes.
  16. Altmetric Badge
    Chapter 15 Clinical Bioinformatics
  17. Altmetric Badge
    Chapter 16 Natural language processing in biomedicine: a unified system architecture overview.
  18. Altmetric Badge
    Chapter 17 Candidate gene discovery and prioritization in rare diseases.
  19. Altmetric Badge
    Chapter 18 Computer-Aided Drug Designing
Attention for Chapter 6: Studying cancer genomics through next-generation DNA sequencing and bioinformatics.
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Chapter title
Studying cancer genomics through next-generation DNA sequencing and bioinformatics.
Chapter number 6
Book title
Clinical Bioinformatics
Published in
Methods in molecular biology, May 2014
DOI 10.1007/978-1-4939-0847-9_6
Pubmed ID
Book ISBNs
978-1-4939-0846-2, 978-1-4939-0847-9
Authors

Doyle MA, Li J, Doig K, Fellowes A, Wong SQ, Doyle, Maria A., Li, Jason, Doig, Ken, Fellowes, Andrew, Wong, Stephen Q., Maria A. Doyle, Jason Li, Ken Doig, Andrew Fellowes, Stephen Q. Wong

Abstract

Cancer is a complex disease driven by multiple mutations acquired over the lifetime of the cancer cells. These alterations, termed somatic mutations to distinguish them from inherited germline mutations, can include single-nucleotide substitutions, insertions, deletions, copy number alterations, and structural rearrangements. A patient's cancer can contain a combination of these aberrations, and the ability to generate a comprehensive genetic profile should greatly improve patient diagnosis and treatment. Next-generation sequencing has become the tool of choice to uncover multiple cancer mutations from a single tumor source, and the falling costs of this rapid high-throughput technology are encouraging its transition from basic research into a clinical setting. However, the detection of mutations in sequencing data is still an evolving area and cancer genomic data requires some special considerations. This chapter discusses these aspects and gives an overview of current bioinformatics methods for the detection of somatic mutations in cancer sequencing data.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 4%
Denmark 1 4%
Canada 1 4%
Unknown 22 88%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 5 20%
Researcher 5 20%
Student > Ph. D. Student 3 12%
Student > Master 2 8%
Other 2 8%
Other 3 12%
Unknown 5 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 28%
Biochemistry, Genetics and Molecular Biology 6 24%
Medicine and Dentistry 4 16%
Environmental Science 1 4%
Neuroscience 1 4%
Other 0 0%
Unknown 6 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 June 2014.
All research outputs
#14,196,440
of 22,756,196 outputs
Outputs from Methods in molecular biology
#4,170
of 13,089 outputs
Outputs of similar age
#120,327
of 227,220 outputs
Outputs of similar age from Methods in molecular biology
#26
of 129 outputs
Altmetric has tracked 22,756,196 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,089 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 64% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 227,220 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 129 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.