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Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns.

Maria OsmalaHarri Lähdesmäki
Published in: BMC bioinformatics (2020)
PREPRINT performs favorably to the state-of-the-art methods, especially when requiring the methods to predict a larger set of enhancers. PREPRINT generalises successfully to data from cell type not utilised for training, and often the PREPRINT performs better than the previous methods. The PREPRINT enhancers are less sensitive to the choice of prediction threshold. PREPRINT identifies biologically validated enhancers not predicted by the competing methods. The enhancers predicted by PREPRINT can aid the genome interpretation in functional genomics and clinical studies.
Keyphrases
  • genome wide
  • transcription factor
  • endothelial cells
  • machine learning
  • gene expression
  • deep learning
  • big data
  • binding protein