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GAPGOM-an R package for gene annotation prediction using GO Metrics.

Casper van MourikRezvan EhsaniFinn Drabløs
Published in: BMC research notes (2021)
GAPGOM integrates two relevant algorithms, lncRNA2GOA and TopoICSim, into a user-friendly R package. Here lncRNA2GOA does annotation prediction by co-expression, whereas TopoICSim estimates similarity between GO graphs, which can be used for benchmarking of prediction performance, but also for comparison of GO graphs in general. The package provides an improved implementation of the original tools, with substantial improvements in performance and documentation, unified interfaces, and additional features.
Keyphrases
  • long non coding rna
  • poor prognosis
  • machine learning
  • healthcare
  • long noncoding rna
  • rna seq
  • genome wide
  • dna methylation
  • single cell
  • genome wide analysis