Even Moa Myklebust

Student - Faculty of Medicine

Norwegian version of this page

Email e.m.myklebust@medisin.uio.no

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Currently working on Bayesian nonlinear mixed effect models for relapse prediction in Multiple Myeloma.

PhenoPop workflow

In 2023, we developed a new method called PhenoPop that reliably identifies tumor subpopulations exhibiting differential drug responses, and estimates their drug-sensitivities and frequencies within the bulk.

Academic interests

Hierarchical Bayesian modeling
Knowledge-driven statistical learning
Personalized medicine
Multiple Myeloma

Background

MSc (2020) in Industrial Mathematics at the Norwegian University of Science and Technology (NTNU). Master thesis on a model for latent variable inference in neuroscience supervised by Benjamin Dunn.

Collaborators

Alvaro Köhn-Luque and Arnoldo Frigessi at Oslo Centre of Biostatistics and Epidemiology
Jasmine Foo and Kevin Leder at the University of Minnesota, USA
Fredrik Schjesvold at the KG Jebsen Center for B-Cell Malignancies and Oslo Myeloma Center, University of Oslo
Kjetil Taskén and Jorrit Enserink, Institute for Cancer Research at Oslo University Hospital
Helle Skjerven, Drammen sykehus

Pages

Tags: Biostatistics, Machine Learning, Computational Biology, Cancer, Stochastic Modeling, Statistical inference, Personalised Cancer Therapies, Latent variable models, Computational intensive statistics

Wu, Chenyu; Gunnarsson, Einar Bjarki; Myklebust, Even Moa; Luque, Alvaro Kohn; Tadele, Dagim Shiferaw & Enserink, Jorrit [Show all 9 contributors for this article] (2024). Using birth-death processes to infer tumor subpopulation structure from live-cell imaging drug screening data. PLoS Computational Biology. ISSN 1553-734X. 20(3). doi: 10.1371/journal.pcbi.1011888.
Skjerven, Helle; Myklebust, Even Moa; Korvald, Christian; Stubberud, Kjetil; Hovda, Tone & Porojnicu, Alina Carmen [Show all 10 contributors for this article] (2023). Long-term follow-up of complex oncoplastic breast-conserving surgery, standard breast conservation and skin-sparing mastectomy in DCIS - a register-based study. European Journal of Surgical Oncology. ISSN 0748-7983. 50(2). doi: 10.1016/j.ejso.2023.107938.
Kohn Luque, Alvaro; Myklebust, Even Moa; Tadele, Dagim Shiferaw; Giliberto, Mariaserena; Schmiester, Leonard & Noory, Jasmine [Show all 15 contributors for this article] (2023). Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data. Cell Reports Methods. 3(3). doi: 10.1016/j.crmeth.2023.100417. Full text in Research Archive
Skjerven, Helle; Myklebust, Even Moa; Korvald, Christian; Porojnicu, Alina Carmen; Kåresen, Rolf & Hofvind, Solveig Sand-Hanssen [Show all 8 contributors for this article] (2022). Oncological outcomes after simple and skin-sparing mastectomy of ductal carcinoma in situ: A register-based cohort study of 576 Norwegian women. European Journal of Surgical Oncology. ISSN 0748-7983. doi: 10.1016/j.ejso.2022.11.594.

View all works in 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.

View all works in Cristin

Published Mar. 15, 2023 3:53 PM - Last modified Dec. 20, 2023 11:27 AM