Mixed integer linear programming based machine learning approach identifies regulators of telomerase in yeast

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...

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Main Authors: Poos, Alexandra (Author) , Maicher, André (Author) , Dieckmann, Anna Katharina (Author) , Oswald, Marcus (Author) , Eils, Roland (Author) , Kupiec, Martin (Author) , Luke, Brian (Author) , König, Rainer (Author)
Format: Article (Journal)
Language:English
Published: 22 February 2016
In: Nucleic acids research
Year: 2016, Volume: 44, Issue: 10
ISSN:1362-4962
Online Access: Get full text
Author Notes:Alexandra M. Poos, André Maicher, Anna K. Dieckmann, Marcus Oswald, Roland Eils, Martin Kupiec, Brian Luke and Rainer König

MARC

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520 |a 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. 
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650 4 |a Gene Regulatory Networks 
650 4 |a Histones 
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650 4 |a Nuclear Proteins 
650 4 |a Programming, Linear 
650 4 |a Repressor Proteins 
650 4 |a Reproducibility of Results 
650 4 |a Saccharomyces cerevisiae 
650 4 |a Saccharomyces cerevisiae Proteins 
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