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

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 be

SUBMITTER: Poos AM 

PROVIDER: S-EPMC4889924 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature