Even Moa Myklebust

Faglige interesser

  • Hierarkiske Bayesianske modeller
  • Kunnskapsdrevet maskinlæring
  • Persontilpasset kreftbehandling
  • Myelomatose 

    PhenoPop workflow

    Vi utviklet en ny metode kalt PhenoPop som identifiserer tumor-subpopulasjoner med ulik respons på medisinering. Se vår preprint for detaljer.

    Bakgrunn

    MSc (2020) i Industriell Matematikk ved Norges teknisk-naturvitenskapelige universitet (NTNU). Masteravhandling om en modell for latent variabel-inferens innen nevrovitenskap veiledet av Benjamin Dunn

    Samarbeid

    Nettsider

    Emneord: Biostatistikk, Maskinlæring, Computational Biology, Kreft, Stokastisk modellering, Statistisk inferens, Personalised Cancer Therapies, Latent variable models, Computational intensive statistics

    Publikasjoner

    Se alle arbeider i Cristin

    • Myklebust, Even Moa (2023). Skin-sparing vs simple mastectomy for DCIS.
    • Myklebust, Even Moa (2023). Predicting treatment response in Multiple Myeloma by combining mechanistic modeling with statistical learning in a Hierarchical Bayesian framework.
    • Myklebust, Even Moa (2023). A framework for personalized prognosis of tumor evolution in Multiple Myeloma.
    • Myklebust, Even Moa (2023). Predicting treatment response in Multiple Myeloma by combining mechanistic modeling with statistical learning in a Hierarchical Bayesian framework.
    • Myklebust, Even Moa (2023). Predicting Progression Free Survival in Multiple Myeloma with a Hierarchical Bayesian model.
    • Myklebust, Even Moa (2023). Personalized treatment recommendations for Multiple Myeloma with a Hierarchical Bayesian model.
    • Myklebust, Even Moa (2023). Relapse prediction in Multiple Myeloma patients through Mathematical modeling.
    • Myklebust, Even Moa (2023). Relapse prediction in Multiple Myeloma patients through Mathematical modeling.
    • Myklebust, Even Moa (2022). Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data.
    • Myklebust, Even Moa (2022). Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data.
    • Myklebust, Even Moa (2022). A framework for personalized prognosis of tumor evolution in Multiple Myeloma by multi-output statistical learning.
    • Myklebust, Even Moa (2022). Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data.
    • Myklebust, Even Moa (2022). Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data.
    • Myklebust, Even Moa (2021). Phenotypic deconvolution of cancer drug screens.

    Se alle arbeider i Cristin

    Publisert 15. mars 2023 15:53 - Sist endret 24. mai 2023 13:52