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Discovery of clinically relevant fusions in pediatric cancer.

Stephanie LaHayeJames R FitchKyle J VoytovichAdam C HermanBenjamin J KellyGrant E LammiJeremy A ArbesfeldSaranga WijeratneSamuel J FranklinKathleen M SchiefferNatalie BirSean D McGrathAnthony R MillerAmy WetzelKatherine E MillerTracy A BedrosianKristen LeraasElizabeth A VargaKristy LeeAjay GuptaBhuvana SettyDaniel R BouéJeffrey R LeonardJonathan L FinlayMohamed S AbdelbakiDiana S OsorioSelene C KooDaniel C KoboldtAlex H WagnerAnn-Kathrin EisfeldKrzysztof MrózekVincent MagriniCatherine E CottrellElaine R MardisRichard K WilsonPeter White
Published in: BMC genomics (2021)
The EnFusion pipeline offers a streamlined approach to discover fusions in cancer, at higher levels of sensitivity and accuracy than single algorithm methods. Furthermore, this method accurately identifies driver fusions in pediatric cancer, providing clinical impact by contributing evidence to diagnosis and, when appropriate, indicating targeted therapies.
Keyphrases
  • papillary thyroid
  • squamous cell
  • childhood cancer
  • machine learning
  • squamous cell carcinoma
  • small molecule
  • lymph node metastasis
  • deep learning
  • gene expression
  • young adults
  • dna methylation
  • single cell
  • neural network