Our group develops and applies advanced statistical and machine-learning methods to understand biological processes across scales, from molecular mechanisms to population-level patterns. We address complementary parts of this continuum, including molecular modelling, systems biology, registry-based research, psychometrics, and population genetics. Across these areas, our work lies at the intersection of high-dimensional statistics, machine learning, Bayesian inference and mechanistic modelling.
Ayuda-Durán P, Hanes R, Rønneberg L, Alver TN, Øy GF, Hauge S, Andersen AN, Egeland EV, Juell S, Nakken S, Asenso TQ, Zucknick M, Syljuåsen RG, Mælandsmo GM, Hovig E, Enserink JM(2026) Systematic interrogation of drug sensitivities in melanoma reveals potent synergistic and antagonistic drug combinations with translational potential Commun Med (Lond), 6(1) DOI 10.1038/s43856-026-01583-w, PubMed 42062484
Birkeli G, Thomas OMT, Ballangrud R, Deilkås ECT, Lindahl AK(2026) No effect of learning from excellence on patient safety culture in a postanaesthesia care unit: a longitudinal quasi-experimental study BMC Health Serv Res, 26(1) DOI 10.1186/s12913-026-15405-1, PubMed 42786463
Bjarkø VV, Wolford BN, Haug EB, Stene LC, Carlsson S, Langhammer A, Birkeland KI, Berg TJ, Sørgjerd EP, Lyssenko V, Brumpton BM, Vie GÅ, Åsvold BO(2026) Temporal changes and genetic susceptibility to type 2 diabetes (1984-2019; HUNT): a longitudinal, population-based study Lancet Diabetes Endocrinol, 14(3), 223-232 DOI 10.1016/S2213-8587(25)00322-5, PubMed 41619756