ArticleNucleic acids research2016
Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast.
Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- Article
- Mathematical Programming for Modeling Expression of a Gene Using Gurobi Optimizer to Identify Its Transcriptional Regulators.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Reprogramming of macrophages employing gene regulatory and metabolic network models.PLoS computational biology · 2020Article
- Modelling TERT regulation across 19 different cancer types based on the MIPRIP 2.0 gene regulatory network approach.BMC bioinformatics · 2019Article
- MiR-192, miR-200c and miR-17 are fibroblast-mediated inhibitors of colorectal cancer invasion.Oncotarget · 2018Article
- REGGAE: a novel approach for the identification of key transcriptional regulators.Bioinformatics (Oxford, England) · 2018Article
- RegulatorTrail: a web service for the identification of key transcriptional regulators.Nucleic acids research · 2017Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
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