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Exploring patterns of epigenetic information with data mining techniques.

Overview of attention for article published in Current Pharmaceutical Design, January 2013
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Article details
Title
Exploring patterns of epigenetic information with data mining techniques.
Published in
Current Pharmaceutical Design, January 2013
DOI 10.2174/138161213804581936
Pubmed ID
Authors
Abstract

Data mining, a part of the Knowledge Discovery in Databases process (KDD), is the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management. Analyses of epigenetic data have evolved towards genome-wide and high-throughput approaches, thus generating great amounts of data for which data mining is essential. Part of these data may contain patterns of epigenetic information which are mitotically and/or meiotically heritable determining gene expression and cellular differentiation, as well as cellular fate. Epigenetic lesions and genetic mutations are acquired by individuals during their life and accumulate with ageing. Both defects, either together or individually, can result in losing control over cell growth and, thus, causing cancer development. Data mining techniques could be then used to extract the previous patterns. This work reviews some of the most important applications of data mining to epigenetics.

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X Demographics

X Demographics

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

Mendeley demographics

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 2 33%
Professor 1 17%
Unknown 3 50%
Readers by discipline
Readers by discipline Count As %
Computer Science 2 33%
Medicine and Dentistry 1 17%
Unknown 3 50%
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 15 July 2017.
All research outputs
#22,952,148
of 34,365,356 outputs
Outputs from Current Pharmaceutical Design
#3,208
of 4,562 outputs
Outputs of similar age
#244,688
of 345,978 outputs
Outputs of similar age from Current Pharmaceutical Design
#192
of 259 outputs
Altmetric has tracked 34,365,356 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,562 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one is in the 29th percentile – i.e., 29% 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 345,978 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 259 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.