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Classification and Personalized Prognostic Assessment on the Basis of Clinical and Genomic Features in Myelodysplastic Syndromes.

Matteo BersanelliErica TravaglinoManja MeggendorferTommaso MatteuzziClaudia SalaEttore MoscaChiara ChiereghinNoemi Di NanniMatteo GnocchiMatteo ZampiniMarianna RossiGiulia MaggioniAlberto TermaniniEmanuele AngelucciBernardi MassimoLorenza BorinBenedetto BrunoFrancesca BonifaziValeria SantiniAndrea BacigalupoMaria Teresa Teresa VosoEsther Natalie OlivaMarta RivaMarta UbezioLucio MorabitoAlessia CampagnaClaudia SaittaVictor SavevskiEnrico GiampieriDaniel RemondiniFrancesco PassamontiFabio CiceriNiccolò BolliAlessandro RambaldiWolfgang KernShahram KordastiFrancisco SoléLaura PalomoGuillermo F SanzArmando SantoroUwe PlatzbeckerPierre FenauxLuciano MilanesiTorsten HaferlachGastone C CastellaniMatteo Giovanni Della Porta
Published in: Journal of clinical oncology : official journal of the American Society of Clinical Oncology (2021)
Genomic landscape in MDS reveals distinct subgroups associated with specific clinical features and discrete patterns of evolution, providing a proof of concept for next-generation disease classification and prognosis.
Keyphrases
  • deep learning
  • machine learning
  • copy number
  • gene expression
  • dna methylation