| |  Online-Ressource |
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| Verfasst von: | Poos, Alexandra [VerfasserIn]  |
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| | Maicher, André [VerfasserIn] |
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| | Dieckmann, Anna Katharina [VerfasserIn] |
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| | Oswald, Marcus [VerfasserIn] |
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| | Eils, Roland [VerfasserIn] |
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| | Kupiec, Martin [VerfasserIn] |
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| | Luke, Brian [VerfasserIn] |
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| | König, Rainer [VerfasserIn] |
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| Titel: | Mixed integer linear programming based machine learning approach identifies regulators of telomerase in yeast |
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| Verf.angabe: | Alexandra M. Poos, André Maicher, Anna K. Dieckmann, Marcus Oswald, Roland Eils, Martin Kupiec, Brian Luke and Rainer König |
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| E-Jahr: | 2016 |
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| Jahr: | 22 February 2016 |
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| Umfang: | 9 S. |
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| Fussnoten: | Published online 22 February 2016 ; Gesehen am 03.11.2020 |
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| Titel Quelle: | Enthalten in: Nucleic acids research |
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| Ort Quelle: | Oxford : Oxford Univ. Press, 1974 |
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| Jahr Quelle: | 2016 |
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| Band/Heft Quelle: | 44(2016,10), Artikel-Nummer e93, 9 Seiten |
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| ISSN Quelle: | 1362-4962 |
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| 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. |
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| DOI: | doi:10.1093/nar/gkw111 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
DOI: https://doi.org/10.1093/nar/gkw111 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Gene Expression Regulation, Fungal |
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| | Gene Regulatory Networks |
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| | Histones |
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| | Machine Learning |
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| | Mediator Complex |
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| | Mutation |
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| | Nuclear Proteins |
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| | Programming, Linear |
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| | Repressor Proteins |
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| | Reproducibility of Results |
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| | Saccharomyces cerevisiae |
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| | Saccharomyces cerevisiae Proteins |
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| | Sirtuin 2 |
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| | Software |
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| | Telomerase |
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| K10plus-PPN: | 1737690209 |
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| Verknüpfungen: | → Zeitschrift |
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Mixed integer linear programming based machine learning approach identifies regulators of telomerase in yeast / Poos, Alexandra [VerfasserIn]; 22 February 2016 (Online-Ressource)
