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SVM-DO: identification of tumor-discriminating mRNA signatures via support vector machines supported by Disease Ontology.

Mustafa Erhan ÖzerPemra Özbek SaricaKazım Yalçın Arğa
Published in: Turkish journal of biology = Turk biyoloji dergisi (2023)
By combining gene sets for both diagnosis and prognosis, our method can improve clinical applications in cancer research. Our algorithm is available as an R package with a graphical user interface in Bioconductor (https://doi.org/10.18129/B9.bioc.SVMDO) and GitHub (https://github.com/robogeno/SVMDO).
Keyphrases
  • genome wide
  • papillary thyroid
  • machine learning
  • squamous cell
  • deep learning
  • copy number
  • lymph node metastasis
  • dna methylation
  • squamous cell carcinoma
  • young adults
  • transcription factor