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Integration of RNA-Seq data with heterogeneous microarray data for breast cancer profiling.

Daniel CastilloJuan Manuel GálvezLuis Javier HerreraBelén San RománFernando RojasIgnacio Rojas
Published in: BMC bioinformatics (2017)
This work proposes a novel data integration stage in the traditional gene expression analysis pipeline through the combination of heterogeneous data from microarrays and RNA-Seq technologies. Available samples have been successfully classified using a subset of six genes obtained by a feature selection method. Consequently, a new classification and diagnosis tool was built and its performance was validated using previously unseen samples.
Keyphrases
  • rna seq
  • single cell
  • electronic health record
  • big data
  • machine learning
  • deep learning
  • genome wide identification
  • genome wide
  • gene expression
  • young adults
  • artificial intelligence
  • genome wide analysis