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Lung Cancer and Personalized Medicine: Novel Therapies and Clinical Management

Overview of attention for book
Attention for Chapter 7: Next-Generation Sequencing and Applications to the Diagnosis and Treatment of Lung Cancer.
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  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
5 Dimensions

Readers on

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30 Mendeley
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Chapter title
Next-Generation Sequencing and Applications to the Diagnosis and Treatment of Lung Cancer.
Chapter number 7
Book title
Lung Cancer and Personalized Medicine: Novel Therapies and Clinical Management
Published in
Advances in experimental medicine and biology, December 2015
DOI 10.1007/978-3-319-24932-2_7
Pubmed ID
Book ISBNs
978-3-31-924931-5, 978-3-31-924932-2
Authors

Kruglyak, Kristina M, Lin, Erick, Ong, Frank S, Kristina M. Kruglyak Ph.D., Erick Lin M.D., Ph.D., Frank S. Ong M.D., Kristina M. Kruglyak, Erick Lin, Frank S. Ong

Editors

Aamir Ahmad, Shirish M. Gadgeel

Abstract

Cancer is a genetic disease characterized by uncontrolled growth of abnormal cells. Over time, somatic mutations accumulate in the cells of an individual due to replication errors, chromosome segregation errors, or DNA damage. When not caught by traditional mechanisms, these somatic mutations can lead to cellular proliferation, the hallmark of cancer. Lung cancer is the leading cause of cancer-related mortality in the United States, accounting for approximately 160,000 deaths annually. Five year survival rates for lung cancer remain low (<50 %) for all stages, with even worse prognosis (<15 %) in late stage cases. Technological advances, including advances in next-generation sequencing (NGS), offer the vision of personalized medicine or precision oncology, wherein an individual's treatment can be based on his or her individual molecular profile, rather than on historical population-based medicine. Towards this end, NGS has already been used to identify new biomarker candidates for the early diagnosis of lung cancer and is increasingly used to guide personalized treatment decisions. In this review we will provide a high-level overview of NGS technology and summarize its application to the diagnosis and treatment of lung cancer. We will also describe how NGS can drive advances that bring us closer to precision oncology and discuss some of the technical challenges that will need to be overcome in order to realize this ultimate goal.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 5 17%
Student > Postgraduate 4 13%
Student > Master 4 13%
Researcher 4 13%
Librarian 3 10%
Other 6 20%
Unknown 4 13%
Readers by discipline Count As %
Medicine and Dentistry 10 33%
Biochemistry, Genetics and Molecular Biology 5 17%
Agricultural and Biological Sciences 3 10%
Social Sciences 3 10%
Computer Science 2 7%
Other 2 7%
Unknown 5 17%

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 06 May 2016.
All research outputs
#7,644,122
of 12,680,054 outputs
Outputs from Advances in experimental medicine and biology
#1,266
of 3,207 outputs
Outputs of similar age
#167,800
of 352,402 outputs
Outputs of similar age from Advances in experimental medicine and biology
#225
of 657 outputs
Altmetric has tracked 12,680,054 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,207 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 56% 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 352,402 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 657 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.