| 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 |
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. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 33% |
| United States | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 2% |
| Unknown | 44 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 18% |
| Student > Master | 8 | 18% |
| Researcher | 7 | 16% |
| Student > Bachelor | 6 | 13% |
| Student > Doctoral Student | 3 | 7% |
| Other | 7 | 16% |
| Unknown | 6 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 10 | 22% |
| Biochemistry, Genetics and Molecular Biology | 8 | 18% |
| Engineering | 5 | 11% |
| Computer Science | 3 | 7% |
| Medicine and Dentistry | 2 | 4% |
| Other | 7 | 16% |
| Unknown | 10 | 22% |
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
#14,839,167
of 22,852,911 outputs
Outputs from Nucleic Acids Research
#21,779
of 26,339 outputs
Outputs of similar age
#167,249
of 298,746 outputs
Outputs of similar age from Nucleic Acids Research
#150
of 214 outputs
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