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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 Cancer Communications, April 2015
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Title
Cancer research in the era of next‐generation sequencing and big data calls for intelligent modeling
Published in
Cancer Communications, 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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 32%
Other 5 23%
Student > Bachelor 3 14%
Student > Doctoral Student 2 9%
Student > Master 2 9%
Other 2 9%
Unknown 1 5%
Readers by discipline Count As %
Medicine and Dentistry 5 23%
Biochemistry, Genetics and Molecular Biology 3 14%
Agricultural and Biological Sciences 2 9%
Computer Science 2 9%
Engineering 2 9%
Other 6 27%
Unknown 2 9%