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Genomic Classification and Individualized Prognosis in Multiple Myeloma.

Francesco MauraArjun Raj RajannaBachisio ZicchedduAlexandra M PoosAndriy DerkachKylee H MaclachlanMichael A DuranteBenjamin T DiamondMarios PapadimitriouFaith E DaviesEileen Mary BoyleBrian A WalkerMalin L HultcrantzAriosto SilvaOliver HamptonJamie K TeerErin M SiegelNiccolò BolliGraham H JacksonMartin F KaiserCharlotte PawlynCurly T C M MorrisDickran KazandjianCaleb SteinMarta ChesiPeter Leif BergsagelElias Karl MaiHartmut GoldschmidtKatja C WeiselRoland FenkMarc-Steffen RaabFrits Van RheeSaad Z UsmaniKenneth H ShainNiels WeinholdGareth MorganCarl Ola Landgren
Published in: Journal of clinical oncology : official journal of the American Society of Clinical Oncology (2024)
Integrating clinical, demographic, genomic, and therapeutic data, to our knowledge, we have developed the first individualized risk-prediction model enabling personally tailored therapeutic decisions for patients with NDMM.
Keyphrases
  • multiple myeloma
  • copy number
  • machine learning
  • healthcare
  • deep learning
  • electronic health record
  • big data
  • gene expression
  • dna methylation