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Sequencing and curation strategies for identifying candidate glioblastoma treatments.

Mayu O FrankTakahiko KoyamaKahn RhrissorrakraiNicolas RobineFilippo UtroAnne-Katrin EmdeBo-Juen ChenKanika AroraMinita ShahHeather GeigerVanessa FeliceEsra DikogluSadia RahmanAlice FangVladimir VacicEwa A BergmannJulia L Moore VogelCatherine ReevesDepinder KhairaAnthony CalabroDuyang KimMichelle F Lamendola-EsselCecilia EstevesPhaedra AgiusChristian StolteJohn BoockvarAlexis DemopoulosDimitris G PlacantonakisJohn G GolfinosCameron BrennanJeffrey BruceAndrew B LassmanPeter CanollChristian GrommesMariza DarasEli DiamondAntonio OmuroElena PentsovaDana E OrangeStephen J HarveyJerome B PosnerVanessa V MicheliniVaidehi JobanputraMichael C ZodyJohn KellyLaxmi ParidaKazimierz O WrzeszczynskiAjay K RoyyuruRobert B Darnell
Published in: BMC medical genomics (2019)
These results present the first comprehensive comparison of technical and machine augmented analysis of targeted panel and WGS/RNA-seq to identify potential cancer treatments.
Keyphrases
  • rna seq
  • single cell
  • papillary thyroid
  • squamous cell
  • deep learning
  • squamous cell carcinoma
  • lymph node metastasis
  • childhood cancer
  • drug delivery
  • climate change
  • machine learning
  • young adults