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Cancer research in the era of next-generation sequencing and big data calls for intelligent modeling

Overview of attention for article published in Ai zheng Aizheng Chinese journal of cancer, April 2015
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1 tweeter

Citations

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Title
Cancer research in the era of next-generation sequencing and big data calls for intelligent modeling
Published in
Ai zheng Aizheng Chinese journal of cancer, April 2015
DOI 10.1186/s40880-015-0008-8
Pubmed ID
Authors

Jari Yli-Hietanen, Antti Ylipää, Olli Yli-Harja

Abstract

We examine the role of big data and machine learning in cancer research. We describe an example in cancer research where gene-level data from The Cancer Genome Atlas (TCGA) consortium is interpreted using a pathway-level model. As the complexity of computational models increases, their sample requirements grow exponentially. This growth stems from the fact that the number of combinations of variables grows exponentially as the number of variables increases. Thus, a large sample size is needed. The number of variables in a computational model can be reduced by incorporating biological knowledge. One particularly successful way of doing this is by using available gene regulatory, signaling, metabolic, or context-specific pathway information. We conclude that the incorporation of existing biological knowledge is essential for the progress in using big data for cancer research.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 30%
Other 5 25%
Student > Doctoral Student 2 10%
Student > Master 2 10%
Professor 1 5%
Other 3 15%
Unknown 1 5%
Readers by discipline Count As %
Medicine and Dentistry 5 25%
Computer Science 3 15%
Agricultural and Biological Sciences 2 10%
Engineering 2 10%
Biochemistry, Genetics and Molecular Biology 2 10%
Other 4 20%
Unknown 2 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 13 May 2015.
All research outputs
#12,363,495
of 13,974,181 outputs
Outputs from Ai zheng Aizheng Chinese journal of cancer
#176
of 222 outputs
Outputs of similar age
#192,693
of 233,326 outputs
Outputs of similar age from Ai zheng Aizheng Chinese journal of cancer
#1
of 1 outputs
Altmetric has tracked 13,974,181 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 222 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 233,326 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them