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Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast

Overview of attention for article published in Nucleic Acids Research, February 2016
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4 tweeters

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13 Mendeley
Title
Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast
Published in
Nucleic Acids Research, February 2016
DOI 10.1093/nar/gkw111
Pubmed ID
Authors

Poos, Alexandra M, Maicher, André, Dieckmann, Anna K, Oswald, Marcus, Eils, Roland, Kupiec, Martin, Luke, Brian, König, Rainer, Alexandra M. Poos, André Maicher, Anna K. Dieckmann, Marcus Oswald, Roland Eils, Martin Kupiec, Brian Luke, Rainer König

Abstract

Understanding telomere length maintenance mechanisms is central in cancer biology as their dysregulation is one of the hallmarks for immortalization of cancer cells. Important for this well-balanced control is the transcriptional regulation of the telomerase genes. We integrated Mixed Integer Linear Programming models into a comparative machine learning based approach to identify regulatory interactions that best explain the discrepancy of telomerase transcript levels in yeast mutants with deleted regulators showing aberrant telomere length, when compared to mutants with normal telomere length. We uncover novel regulators of telomerase expression, several of which affect histone levels or modifications. In particular, our results point to the transcription factors Sum1, Hst1 and Srb2 as being important for the regulation of EST1 transcription, and we validated the effect of Sum1 experimentally. We compiled our machine learning method leading to a user friendly package for R which can straightforwardly be applied to similar problems integrating gene regulator binding information and expression profiles of samples of e.g. different phenotypes, diseases or treatments.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 23%
Student > Bachelor 2 15%
Student > Master 2 15%
Researcher 2 15%
Professor > Associate Professor 1 8%
Other 3 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 38%
Engineering 3 23%
Biochemistry, Genetics and Molecular Biology 2 15%
Computer Science 2 15%
Unspecified 1 8%
Other 0 0%

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 24 May 2017.
All research outputs
#5,367,549
of 10,197,609 outputs
Outputs from Nucleic Acids Research
#11,345
of 15,431 outputs
Outputs of similar age
#135,407
of 292,158 outputs
Outputs of similar age from Nucleic Acids Research
#208
of 289 outputs
Altmetric has tracked 10,197,609 research outputs across all sources so far. This one is in the 45th percentile – i.e., 45% of other outputs scored the same or lower than it.
So far Altmetric has tracked 15,431 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 24th percentile – i.e., 24% 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 292,158 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 289 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.