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Bioinformatics for precision medicine in oncology: principles and application to the SHIVA clinical trial

Overview of attention for article published in Frontiers in Genetics, May 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

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10 X users
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1 Facebook page
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1 Google+ user

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166 Mendeley
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Title
Bioinformatics for precision medicine in oncology: principles and application to the SHIVA clinical trial
Published in
Frontiers in Genetics, May 2014
DOI 10.3389/fgene.2014.00152
Pubmed ID
Authors

Nicolas Servant, Julien Roméjon, Pierre Gestraud, Philippe La Rosa, Georges Lucotte, Séverine Lair, Virginie Bernard, Bruno Zeitouni, Fanny Coffin, Gérôme Jules-Clément, Florent Yvon, Alban Lermine, Patrick Poullet, Stéphane Liva, Stuart Pook, Tatiana Popova, Camille Barette, François Prud’homme, Jean-Gabriel Dick, Maud Kamal, Christophe Le Tourneau, Emmanuel Barillot, Philippe Hupé

Abstract

Precision medicine (PM) requires the delivery of individually adapted medical care based on the genetic characteristics of each patient and his/her tumor. The last decade witnessed the development of high-throughput technologies such as microarrays and next-generation sequencing which paved the way to PM in the field of oncology. While the cost of these technologies decreases, we are facing an exponential increase in the amount of data produced. Our ability to use this information in daily practice relies strongly on the availability of an efficient bioinformatics system that assists in the translation of knowledge from the bench towards molecular targeting and diagnosis. Clinical trials and routine diagnoses constitute different approaches, both requiring a strong bioinformatics environment capable of (i) warranting the integration and the traceability of data, (ii) ensuring the correct processing and analyses of genomic data, and (iii) applying well-defined and reproducible procedures for workflow management and decision-making. To address the issues, a seamless information system was developed at Institut Curie which facilitates the data integration and tracks in real-time the processing of individual samples. Moreover, computational pipelines were developed to identify reliably genomic alterations and mutations from the molecular profiles of each patient. After a rigorous quality control, a meaningful report is delivered to the clinicians and biologists for the therapeutic decision. The complete bioinformatics environment and the key points of its implementation are presented in the context of the SHIVA clinical trial, a multicentric randomized phase II trial comparing targeted therapy based on tumor molecular profiling versus conventional therapy in patients with refractory cancer. The numerous challenges faced in practice during the setting up and the conduct of this trial are discussed as an illustration of PM application.

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 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 166 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Spain 2 1%
Brazil 1 <1%
Finland 1 <1%
Ireland 1 <1%
France 1 <1%
United Kingdom 1 <1%
Japan 1 <1%
Luxembourg 1 <1%
Other 0 0%
Unknown 154 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 36 22%
Student > Ph. D. Student 34 20%
Student > Master 21 13%
Student > Bachelor 19 11%
Other 13 8%
Other 22 13%
Unknown 21 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 20%
Biochemistry, Genetics and Molecular Biology 32 19%
Medicine and Dentistry 22 13%
Computer Science 14 8%
Engineering 13 8%
Other 19 11%
Unknown 33 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 05 June 2018.
All research outputs
#4,689,268
of 22,755,127 outputs
Outputs from Frontiers in Genetics
#1,465
of 11,758 outputs
Outputs of similar age
#46,512
of 226,629 outputs
Outputs of similar age from Frontiers in Genetics
#27
of 119 outputs
Altmetric has tracked 22,755,127 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,758 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 87% 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 226,629 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 119 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.