Arnoldo Frigessi

• Liu, Qinghua; Vitelli, Valeria; Mannino, Carlo; Frigessi, Arnoldo & Scheel, Ida (2026). Pseudo-Mallows for Efficient Probabilistic Preference Learning. International Conference on Machine Learning (ICML). doi: https:/arxiv.org/pdf/2205.13911. Fulltekst i vitenarkiv
• Azimzade, Youness; Haugen, Mads; Kristensen, Vessela N.; Frigessi, Arnoldo & Luque, Alvaro Kohn (2026). Explainable machine learning-guided integrated multiomics analysis reveals macrophage-driven immune suppression in breast cancer. Nature Communications. 17(1). doi: 10.1038/s41467-026-73617-9. Fulltekst i vitenarkiv Vis sammendrag

  Despite thorough characterizations of cellular compositions within the breast tumor microenvironment (TME), their implications for disease progression and patient prognosis remain poorly understood. Unraveling these effects is vital for identifying potential targets to improve treatment outcomes. In this study, we devise an explainable machine learning (XML) pipeline to scrutinize the associations between TME cellular constituents and relapse-free survival (RFS). By applying this pipeline to estimated cell fractions in the METABRIC and TCGA datasets and comparing these results with associations to pathological complete response (pCR) after neoadjuvant chemotherapy (NAC), we create a comprehensive catalog of the TME’s role based on 5000 patient samples. Our findings reveal an unexpected dichotomy in which macrophages correlate positively with pCR but negatively with RFS, particularly within estrogen receptor-positive (ER+) and Luminal A and B (LumA/B) cancer subtypes. We show that this pattern is driven by heterogeneity in breast tumors characterized by increasing levels of macrophage infiltration. Through imaging mass cytometry (IMC) data analysis, we find that macrophages tend to accumulate in the vicinity of HLA-ABChi epithelial cells as their frequency increases in tumor tissues and that they also express elevated levels of HLA-ABC protein. In both IMC and single-cell RNA sequencing (scRNA-seq) data, we uncover a significant association between these HLA-ABChi macrophages and regulatory and exhausted T cells (TReg and TEx), suggesting their involvement in immune suppression, likely by creating a chronically activated immunosuppressive TME. Subsequent cell-cell communication analysis predicts interactions between HLA-ABChi macrophages and TEx cells via the ligands SIGLEC9, ALCAM, and CSF1, and with TReg cells through APP, ANGPTL4, and SIGLEC9 signaling. Considering the clinical relevance of macrophages in ER+ (LumA/B) subtypes, this work enhances the characterization of macrophage-associated immune suppression in these tumors and identifies potential targets for immunomodulatory strategies.

• Günther, Felix; Brustad, Hilde Kjelgaard; Frigessi, Arnoldo & Britton, Tom (2026). Quantifying the impact of social activities on SARS-CoV-2 transmission using Google mobility reports. PLOS Global Public Health. 6(5 May). doi: 10.1371/journal.pgph.0006368. Fulltekst i vitenarkiv
• Moreira-Soares, Maurício; Fossen, Erlend I. F.; Bilbao-Jayo, Aritz; Almeida, Aitor; Lopez-Perez, Laura & Alonso, Itziar [Vis alle 16 forfattere av denne artikkelen] (2026). Joint probability framework for the development and validation of a prognostic model for the conditional outcome of quality of life: a retrospective study in historical European cohorts of survivors of head and neck cancer. The Lancet Digital Health. 8(5). doi: 10.1016/j.landig.2025.100976. Fulltekst i vitenarkiv
• Kaiser, Daniel; Frigessi, Arnoldo; Ramezani-Kebrya, Ali & Ricaud, Benjamin (2026). CogniLoad: A Synthetic Natural Language Reasoning Benchmark With Tunable Length, Intrinsic Difficulty, and Distractor Density. International Conference on Learning Representations (ICLR). doi: https:/openreview.net/forum?id=0Sex2H5Jnn. Fulltekst i vitenarkiv Vis sammendrag

  Current benchmarks for long-context reasoning in Large Language Models (LLMs) often blur critical factors like intrinsic task complexity, distractor interference, and task length. To enable more precise failure analysis, we introduce CogniLoad, a novel synthetic benchmark grounded in Cognitive Load Theory (CLT). CogniLoad generates natural-language logic puzzles with independently tunable parameters that reflect CLT's core dimensions: intrinsic difficulty ($d$) controls intrinsic load; distractor-to-signal ratio ($\rho$) regulates extraneous load; and task length ($N$) serves as an operational proxy for conditions demanding germane load. Evaluating 22 SotA reasoning LLMs, CogniLoad reveals distinct performance sensitivities, identifying task length as a dominant constraint and uncovering varied tolerances to intrinsic complexity and U-shaped responses to distractor ratios. By offering systematic, factorial control over these cognitive load dimensions, CogniLoad provides a reproducible, scalable, and diagnostically rich tool for dissecting LLM reasoning limitations and guiding future model development.

• Kaiser, Daniel; Ramezani-Kebrya, Ali; Frigessi, Arnoldo & Ricaud, Benjamin (2025). Decomposing Reasoning Efficiency in Large Language Models. International Conference on Learning Representations (ICLR). doi: https:/openreview.net/forum?id=mym1D6mIZR. Fulltekst i vitenarkiv Vis sammendrag

  While large language models (LLMs) are typically evaluated on accuracy metrics alone, real-world deployment requires careful control of computational efficiency. Building on the CogniLoad benchmark, we introduce a unified efficiency metric (i.e., correct answers per 1'000 output tokens) enabling direct cross‑model comparison. We further provide an interpretable factorization into context robustness, logic robustness, and token appetite. Evaluating 15 state‑of‑the‑art reasoning LLMs on CogniLoad, we find that some models fail due to logic errors, others consume excessive tokens, and a few hit context limits. Tokens are an imperfect but practical proxy for computation load, permitting consistent comparisons across closed and open models. By decomposing overall efficiency into actionable components, our framework identifies concrete targets for improving LLM reasoning efficiency.

• Smajdor, Anna; Frigessi, Arnoldo & Zucknick, Manuela (2025). WHAT IS RIGHT FOR ‘THIS’ PATIENT? AUTONOMY, TRUST AND TRANSPARENCY IN THE ERA OF MEDICAL AI. Síntesis: Revista de Filosofia. ISSN 2452-4476. 52(164), s. 611–611. doi: 10.20911/21769389v52n164p611/2025. Fulltekst i vitenarkiv Vis sammendrag

  In this paper we compare the capabilities and limitations of AI algorithms and human doctors in the context of treatment recommendations. Ethical concerns have been raised in the literature about transparency, accountability, and interpretability in relation to AI algorithms in the medical context. Patients’ autonomy is not likely to be respected if treatment recommendations are opaque or incomprehensible. Medical AI algorithms are programmed to prioritise predefined goals that may not align with patients’ preferences, potentially undermining their autonomy without their awareness. Yet human doctors often face similar challenges, such as reliance on clinical trial data with endpoints that may not be fully understood by patients. We conclude that there are opportunities now to integrate patient autonomy considerations into AI algorithms at the design stage, and that these goals and any relevant weightings are made explicit to end users, including both doctors and patients. Keywords: Artificial intelligence. Autonomy Alignment. Medical ethics.

• Lengle, Julie Marie; Zhang, Chi; Frigessi, Arnoldo & Andersen, Lene Frost (2025). Nutritionally adequate, healthy, and climate-friendly diets following the Nordic Nutrition Recommendations 2023: an optimization study for Norway. Frontiers in Nutrition. 12, s. 1–16. doi: 10.3389/fnut.2025.1485287. Fulltekst i vitenarkiv Vis sammendrag

  Background: Reducing red meat consumption is an effective tactic for decreasing environmental impact of diets while maintaining nutritional adequacy, healthiness, and overall consumer acceptability. Still, dietary change in favor of plant foods is a controversial climate mitigation measure, especially in the Nordic region where agri-food heritage is linked to ruminant husbandry. Objective: In this study we aimed to explore sustainable diets for the Norwegian context by (1) investigating the environmental impacts of nutritionally optimized diets following the Nordic Nutrition Recommendations 2023 (NNR2023), (2) estimating potential for environmental impact reduction across scenarios of meat and legume consumption, and (3) identifying nutritional challenges. Methods: Quadratic optimization was employed to minimize departure from the average observed Norwegian diet while meeting nutrient, health, and carbon footprint constraints. The diet of Norwegian adults was estimated based on results from the national dietary survey Norkost 3. Global warming potential (GWP), freshwater and marine eutrophication, terrestrial acidification, water use, and transformation and use of land were calculated using data from the Norwegian Life Cycle Assessment Food Database version 01. Diets were optimized to meet NNR2023 nutrition and health recommendations for nutrients and food groups. Optimizations were first run without constraints on GWP, for three diet scenarios: (1) nutrients and health-based targets for food amounts ( NNR2023 ), (2) nutrients and health-based targets for food amounts with ruminant meat ≥ observed intake (62 g/day) ( Ruminant ), and (3) nutrients and health-based targets for food amounts with legumes content ≥40 g/day (Legumes). Then, GWP constraints were applied in 5% increments until no solution was found. The optimal diet for each scenario was defined as the diet with the largest feasible reduction in GWP (NNR2023+/Ruminant+/Legumes+). Results: Optimizing the diet to meet nutrient and health constraints alone resulted in a modest decrease in GWP (NNR2023); retaining ruminant meat consumption (Ruminant) impeded the reduction (−9% vs. 0%). Diets following NNR2023 nutrient and health constraints alone were feasible up until a 30% reduction in GWP (NNR2023+). A 35% reduction in GWP was achieved when legumes were added to the diet (Legumes+), while diets retaining 62 g of ruminant meat were not identified beyond a 15% reduction in GWP (Ruminant+). Sodium and selenium were the strongest limiting constraints in all scenarios. Diets with a 40% reduction in GWP were identified when nutrient constraints were lowered from the Recommended Intake to the Average Requirement (NNR2023+/Legumes+). Reductions in GWP coincided with reductions in all measured environmental indicators except marine eutrophication. Conclusion: The NNR2023 guidelines outline diets that have generally lower environmental impacts than the average Norwegian diet, though outcomes depend on distribution of meat and legume consumption in the diet. Regardless of degree of environmental impact reduction, diets following NNR2023 guidelines will require significant dietary changes compared to observed intake, including an increase in consumption of fruits, vegetables, and grains, and a strong decrease in consumption of red meat, total meat, and discretionary foods. Preventing the model from removing any ruminant meat from the diet limited GWP reduction to 15% and induced considerable changes in intake of other food groups, especially a decrease in other types of meat.

• Pagui, Euloge Clovis Kenne; Brant, Simon Boge; Svendsen, Kristian; Frigessi, Arnoldo; Helland, Åslaug & Thoresen, Steinar Østerbø [Vis alle 7 forfattere av denne artikkelen] (2025). Comparative Effectiveness of Atezolizumab, Nivolumab and Pembrolizumab in Second‐Line Treatment of Advanced Non–Small Cell Lung Cancer. Pharmacoepidemiology and Drug Safety. ISSN 1053-8569. 34(7). doi: 10.1002/pds.70181. Fulltekst i vitenarkiv Vis sammendrag

  ABSTRACT Purpose This observational study aimed to assess the comparative effectiveness of immunotherapy (IO) in the second‐line setting of advanced non–small cell lung cancer (aNSCLC). Methods Population‐based data was obtained from the Cancer Registry of Norway (CRN) and linked with the hospital data from the Norwegian Patient Registry (NPR) and treatment data from the Norwegian Prescribed Drug Registry (NorPD). Adult patients with aNSCLC diagnosis in Norway in 2015–2021 were included. The final cohort included patients using IO in the second‐line setting after first‐line treatment with platinum‐containing doublet chemotherapy. We performed a target trial emulating key eligibility criteria commonly used in aNSCLC randomised clinical trials (RCTs) and constructed three pair‐wise 1:1 propensity score (PS)‐matched comparison cohorts: atezolizumab versus pembrolizumab, atezolizumab versus nivolumab and nivolumab versus pembrolizumab. Age, sex, morphology, ECOG performance status, PD‐L1 expression level, chemoradiation use and comorbidities were included in the PS. The primary outcome was overall survival (OS). Patients were followed until experiencing death, emigration or end of study. A Cox proportional hazards regression model was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results Analyses included a total of 447 patients, 206 treated with atezolizumab, 71 nivolumab and 170 pembrolizumab. The median follow‐up time was 8.4 (IQR: 3.4–20.7) months. We found no evidence of significant differences in OS in any of the three pair‐wise comparisons; atezolizumab versus pembrolizumab (HR: 0.86; 95% CI: 0.65–1.12), atezolizumab versus nivolumab (HR: 0.97; 95% CI: 0.68–1.37) and nivolumab versus pembrolizumab (HR: 0.99; 95% CI: 0.67–1.49). Conclusion The study suggests no evidence of significant differences between the three IO drugs atezolizumab, nivolumab and pembrolizumab when used in second‐line aNSCLC. However, wide CIs and moderate sample sizes limit definitive conclusions. These results highlight the need for larger studies to confirm potential equivalence or detect meaningful differences.

• Griesbauer, Elisabeth Maria; Czado, Claudia; Frigessi, Arnoldo & Haff, Ingrid Hobæk (2025). TVineSynth: A Truncated C-Vine Copula Generator of Synthetic Tabular Data to Balance Privacy and Utility. Proceedings of Machine Learning Research (PMLR). 258, s. 3511–3519. doi: https:/raw.githubusercontent.com/mlresearch/v258/main/assets/griesbauer25a/griesbauer25a.pdf. Fulltekst i vitenarkiv Vis sammendrag

  We propose TVineSynth, a vine copula based synthetic tabular data generator, which is designed to balance privacy and utility, using the vine tree structure and its truncation to do the trade-off. Contrary to synthetic data generators that achieve DP by globally adding noise, TVineSynth performs a controlled approximation of the estimated data generating distribution, so that it does not suffer from poor utility of the resulting synthetic data for downstream prediction tasks. TVineSynth introduces a targeted bias into the vine copula model that, combined with the specific tree structure of the vine, causes the model to zero out privacy-leaking dependencies while relying on those that are beneficial for utility. Privacy is here measured with membership (MIA) and attribute inference attacks (AIA). Further, we theoretically justify how the construction of TVineSynth ensures AIA privacy under a natural privacy measure for continuous sensitive attributes. When compared to competitor models, with and without DP, on simulated and on real-world data, TVineSynth achieves a superior privacy-utility balance.

• Pérez, Tomás Fernando Varnet; Øvergaard, Kristin Romvig; Frigessi, Arnoldo & Biele, Guido (2025). Long-term effect of pharmacological treatment on academic achievement of Norwegian children diagnosed with ADHD: a target trial emulation. International Journal of Epidemiology. ISSN 0300-5771. 54(2), s. 1–11. doi: 10.1093/ije/dyaf010. Fulltekst i vitenarkiv Vis sammendrag

  Background Attention-deficit/hyperactivity disorder (ADHD) is one of the most commonly diagnosed mental disorders in children. For many patients, treatment involves long-term medication in order to reduce symptoms, regulate behaviour, and, hopefully, improve school performance and achievement. However, there is little to no evidence to support a long-term effect on the latter complex outcomes. Methods We utilize a target trial framework to emulate a pretest–posttest control group design and estimate the intention-to-treat effect of ADHD medication on national test scores in children diagnosed with ADHD born between 2000 and 2007 in Norway. The data were obtained through linkage of Norwegian registries (NorPD, Norwegian Prescription Database; NPR, Norwegian Patient Registry; KUHR, Database for Control and Payment of Health Reimbursement; SSB, Statistics Norway; MBRN, Medical Birth Registry of Norway). Results The resulting analytic sample size consisted of 8548 children diagnosed with ADHD, with about 9% missingness in their grade eight national test scores. We find that initiating ADHD medication had a slight positive average effect on national test scores for all three domains: English, numeracy, and reading [standardized mean differences: 0.037 (95% compatibility interval (CI95), −0.003; 0.076), 0.063 (CI95, 0.016; 0.111), 0.071 (CI95, 0.030; 0.111), respectively]. Conclusion We conclude that the estimated long-term average effect of ADHD medication on learning, as measured by the Norwegian national tests, is not clinically relevant. Study strengths include the use of real-world data on ecologically valid and relevant outcomes and the robustness of results across model specifications. Limitations include possibility of unobserved confounding and lack of prescription data.

• Schmiester, Leonard; Brasó-Maristany, Fara; González-Farré, Blanca; Pascual, Tomás; Gavilá, Joaquín & Tekpli, Xavier [Vis alle 11 forfattere av denne artikkelen] (2024). Computational Model Predicts Patient Outcomes in Luminal B Breast Cancer Treated with Endocrine Therapy and CDK4/6 Inhibition. Clinical Cancer Research. ISSN 1078-0432. 30(17), s. 3779–3787. doi: 10.1158/1078-0432.CCR-24-0244. Fulltekst i vitenarkiv Vis sammendrag

  Purpose: Development of a computational biomarker to predict, prior to treatment, the response to CDK4/6 inhibition (CDK4/6i) in combination with endocrine therapy in patients with breast cancer. Experimental Design: A mechanistic mathematical model that accounts for protein signaling and drug mechanisms of action was developed and trained on extensive, publicly available data from breast cancer cell lines. The model was built to provide a patient-specific response score based on the expression of six genes (CCND1, CCNE1, ESR1, RB1, MYC, and CDKN1A). The model was validated in five independent cohorts of 148 patients in total with early-stage or advanced breast cancer treated with endocrine therapy and CDK4/6i. Response was measured either by evaluating Ki67 levels and PAM50 risk of relapse (ROR) after neoadjuvant treatment or by evaluating progression-free survival (PFS). Results: The model showed significant association with patient’s outcomes in all five cohorts. The model predicted high Ki67 [area under the curve; AUC (95% confidence interval, CI) of 0.80 (0.64–0.92), 0.81 (0.60–1.00) and 0.80 (0.65–0.93)] and high PAM50 ROR [AUC of 0.78 (0.64–0.89)]. This observation was not obtained in patients treated with chemotherapy. In the other cohorts, patient stratification based on the model prediction was significantly associated with PFS [hazard ratio (HR) = 2.92 (95% CI, 1.08–7.86), P = 0.034 and HR = 2.16 (1.02 4.55), P = 0.043]. Conclusions: A mathematical modeling approach accurately predicts patient outcome following CDK4/6i plus endocrine therapy that marks a step toward more personalized treatments in patients with Luminal B breast cancer.

• Chan, Yat Hin; Rø, Gunnar; Midtbø, Jørgen E.; Ruscio, Francesco Di; Watle, Sara Sofie Viksmoen & Juvet, Lene Kristine [Vis alle 19 forfattere av denne artikkelen] (2024). Modeling geographic vaccination strategies for COVID-19 in Norway. PLoS Computational Biology. ISSN 1553-734X. 20(1). doi: 10.1371/journal.pcbi.1011426. Fulltekst i vitenarkiv Vis sammendrag

  Vaccination was a key intervention in controlling the COVID-19 pandemic globally. In early 2021, Norway faced significant regional variations in COVID-19 incidence and prevalence, with large differences in population density, necessitating efficient vaccine allocation to reduce infections and severe outcomes. This study explored alternative vaccination strategies to minimize health outcomes (infections, hospitalizations, ICU admissions, deaths) by varying regions prioritized, extra doses prioritized, and implementation start time. Using two models (individual-based and meta-population), we simulated COVID-19 transmission during the primary vaccination period in Norway, covering the first 7 months of 2021. We investigated alternative strategies to allocate more vaccine doses to regions with a higher force of infection. We also examined the robustness of our results and highlighted potential structural differences between the two models. Our findings suggest that early vaccine prioritization could reduce COVID-19 related health outcomes by 8% to 20% compared to a baseline strategy without geographic prioritization. For minimizing infections, hospitalizations, or ICU admissions, the best strategy was to initially allocate all available vaccine doses to fewer high-risk municipalities, comprising approximately one-fourth of the population. For minimizing deaths, a moderate level of geographic prioritization, with approximately one-third of the population receiving doubled doses, gave the best outcomes by balancing the trade-off between vaccinating younger people in high-risk areas and older people in low-risk areas. The actual strategy implemented in Norway was a two-step moderate level aimed at maintaining the balance and ensuring ethical considerations and public trust. However, it did not offer significant advantages over the baseline strategy without geographic prioritization. Earlier implementation of geographic prioritization could have more effectively addressed the main wave of infections, substantially reducing the national burden of the pandemic.

• Wu, Chenyu; Gunnarsson, Einar Bjarki; Myklebust, Even Moa; Luque, Alvaro Kohn; Tadele, Dagim Shiferaw & Enserink, Jorrit [Vis alle 9 forfattere av denne artikkelen] (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. Fulltekst i vitenarkiv Vis sammendrag

  Tumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response characteristics. Characterizing this heterogeneity by determining the subpopulation structure within a tumor enables more precise and successful treatment strategies. In our prior work, we developed PhenoPop, a computational framework for unravelling the drug-response subpopulation structure within a tumor from bulk high-throughput drug screening data. However, the deterministic nature of the underlying models driving PhenoPop restricts the model fit and the information it can extract from the data. As an advancement, we propose a stochastic model based on the linear birth-death process to address this limitation. Our model can formulate a dynamic variance along the horizon of the experiment so that the model uses more information from the data to provide a more robust estimation. In addition, the newly proposed model can be readily adapted to situations where the experimental data exhibits a positive time correlation. We test our model on simulated data (in silico) and experimental data (in vitro), which supports our argument about its advantages.

• Yildiz, Tugba Akman; Arendt, Lisa M.; Geisler, Jürgen; Kristensen, Vessela N.; Frigessi, Arnoldo & Luque, Alvaro Kohn (2024). Modeling of Mouse Experiments Suggests that Optimal Anti-Hormonal Treatment for Breast Cancer is Diet-Dependent. Bulletin of Mathematical Biology. ISSN 0092-8240. 86:42(4), s. 1–33. doi: 10.1007/s11538-023-01253-1. Fulltekst i vitenarkiv Vis sammendrag

  Estrogen receptor positive breast cancer is frequently treated with anti-hormonal treatment such as aromatase inhibitors (AI). Interestingly, a high body mass index has been shown to have a negative impact on AI efficacy, most likely due to disturbances in steroid metabolism and adipokine production. Here, we propose a mathematical model based on a system of ordinary differential equations to investigate the effect of high-fat diet on tumor growth. We inform the model with data from mouse experiments, where the animals are fed with high-fat or control (normal) diet. By incorporating AI treatment with drug resistance into the model and by solving optimal control problems we found differential responses for control and high-fat diet. To the best of our knowledge, this is the first attempt to model optimal anti-hormonal treatment for breast cancer in the presence of drug resistance. Our results underline the importance of considering high-fat diet and obesity as factors influencing clinical outcomes during anti-hormonal therapies in breast cancer patients.

• Moreira-Soares, Maurício; Fossen, Erlend Ignacio Fleck; Taylor, Katherine; Singer, Susanne; Hurley, Katrina & Thomas, Steve [Vis alle 16 forfattere av denne artikkelen] (2024). Quality of life in head and neck cancer survivors: the Big Data for Quality of Life study. medRxiv. doi: 10.1101/2024.02.02.24302118. Fulltekst i vitenarkiv
• Fossen, Erlend Ignacio Fleck; Moreira-Soares, Maurício; Leblanc, Marissa Erin; Frigessi, Arnoldo; Hovig, Eivind & Lopez-Perez, Laura [Vis alle 20 forfattere av denne artikkelen] (2024). External validation of prognostic and predictive gene signatures in head and neck cancer patients. medRxiv. doi: 10.1101/2024.09.24.24314278. Fulltekst i vitenarkiv
• Moreira-Soares, Maurício; Fossen, Erlend Ignacio Fleck; Bilbao-Jayo, Aritz; Almeida, Aitor; Lopez-Perez, Laura & Alonso, Itziar [Vis alle 17 forfattere av denne artikkelen] (2024). Joint probability approach for prognostic prediction of conditional outcomes: application to quality of life in head and neck cancer survivors. medRxiv. Fulltekst i vitenarkiv
• Kamineni, Meghana; Engø-Monsen, Kenth; Midtbø, Jørgen E.; Forland, Frode; Blasio, Birgitte Freiesleben De & Frigessi, Arnoldo [Vis alle 7 forfattere av denne artikkelen] (2023). Effects of non-compulsory and mandatory COVID-19 interventions on travel distance and time away from home, Norway, 2021. Eurosurveillance. ISSN 1025-496X. 28(17). doi: 10.2807/1560-7917.ES.2023.28.17.2200382. Fulltekst i vitenarkiv Vis sammendrag

  Background Given the societal, economic and health costs of COVID-19 non-pharmaceutical interventions (NPI), it is important to assess their effects. Human mobility serves as a surrogate measure for human contacts and compliance with NPI. In Nordic countries, NPI have mostly been advised and sometimes made mandatory. It is unclear if making NPI mandatory further reduced mobility. Aim We investigated the effect of non-compulsory and follow-up mandatory measures in major cities and rural regions on human mobility in Norway. We identified NPI categories that most affected mobility. Methods We used mobile phone mobility data from the largest Norwegian operator. We analysed non-compulsory and mandatory measures with before–after and synthetic difference-in-differences approaches. By regression, we investigated the impact of different NPI on mobility. Results Nationally and in less populated regions, time travelled, but not distance, decreased after follow-up mandatory measures. In urban areas, however, distance decreased after follow-up mandates, and the reduction exceeded the decrease after initial non-compulsory measures. Stricter metre rules, gyms reopening, and restaurants and shops reopening were significantly associated with changes in mobility. Conclusion Overall, distance travelled from home decreased after non-compulsory measures, and in urban areas, distance further decreased after follow-up mandates. Time travelled reduced more after mandates than after non-compulsory measures for all regions and interventions. Stricter distancing and reopening of gyms, restaurants and shops were associated with changes in mobility.

• Storvik, Geir Olve; Palomares, Alfonso Diz-Lois; Engebretsen, Solveig; Rø, Gunnar Øyvind Isaksson; Engø-Monsen, Kenth & Kristoffersen, Anja Bråthen [Vis alle 8 forfattere av denne artikkelen] (2023). A sequential Monte Carlo approach to estimate a time-varying reproduction number in infectious disease models: the Covid-19 case. Journal of the Royal Statistical Society, Series A (Statistics in Society). ISSN 0964-1998. 186(4), s. 616–632. doi: 10.1093/jrsssa/qnad043. Fulltekst i vitenarkiv Vis sammendrag

  Abstract The Covid-19 pandemic has required most countries to implement complex sequences of non-pharmaceutical interventions, with the aim of controlling the transmission of the virus in the population. To be able to take rapid decisions, a detailed understanding of the current situation is necessary. Estimates of time-varying, instantaneous reproduction numbers represent a way to quantify the viral transmission in real time. They are often defined through a mathematical compartmental model of the epidemic, like a stochastic SEIR model, whose parameters must be estimated from multiple time series of epidemiological data. Because of very high dimensional parameter spaces (partly due to the stochasticity in the spread models) and incomplete and delayed data, inference is very challenging. We propose a state-space formalization of the model and a sequential Monte Carlo approach which allow to estimate a daily-varying reproduction number for the Covid-19 epidemic in Norway with sufficient precision, on the basis of daily hospitalization and positive test incidences. The method was in regular use in Norway during the pandemics and appears to be a powerful instrument for epidemic monitoring and management.

• Kabeta, Teshome; Holst, Rene; Wondafrash, Beyene; Frigessi, Arnoldo & Gebremariam, Mekdes (2023). Determinants of household dietary diversity in rural Ethiopia: A household panel study. Journal of Agriculture and Food Research. 12. doi: 10.1016/j.jafr.2023.100550. Fulltekst i vitenarkiv Vis sammendrag

  Background Diet diversification ensures nutritional security and hence helps in meeting nutrient requirements in humans. Despite its importance, diet diversification remains a challenge, especially in rural communities in developing countries. This study aimed to identify the determinants of DD in rural Ethiopia. Methods In this study, we used an agricultural and nutrition household panel data. This study collected data from 1200 households twice a year for two years in nine districts. The household dietary diversity score (HDDS) was used as the outcome measure. Several potential demographic, social, economic, and geographic determinants of HDDS were assessed. Mixed effects truncated Poisson regression was used to identify which of these determinants were associated with HDDS at household level. Result The HDDS value ranged from 1 to 10 with a mean of 5.52 and standard deviation (SD) of 1.54. A unit increment in the variety of crop production gave a 3.4% [95% CI: 1.9%, 4.9%,] increase in relative change (RC) of HDDS. A birr increases in income generated from livestock products resulted in a 2.6% [1.5%, 3.7%] increase in RC of HDDS. The post-harvesting season contributed to a 6.4% [3.6%, 9.3%] increase in RC of HDDS compared to the pre-harvesting. Households headed by a woman had an HDDS that was −7.9% [-12.5%, −3.3%] lower in RC of HDDS than male-lead households. An additional year of education of the household head resulted in a 1.2% [0.7%, 1.7%] increase in RC of HDDS. As the mean women's decision-making score increased by one point, the RC in HDDS increased by 3.8% [1.1%, 6.4%]. No significant association was found between the time taken to reach local markets and HDDS. Conclusion This study identified economic, educational, social, and seasonal factors associated with HDDS, which should be considered when planning interventions aimed at improving HDDS in rural Ethiopia or elsewhere.

• Engebretsen, Solveig; Palomares, Alfonso Diz-Lois; Rø, Gunnar Øyvind Isaksson; Kristoffersen, Anja Bråthen; Lindstrøm, Jonas Christoffer & Engø-Monsen, Kenth [Vis alle 15 forfattere av denne artikkelen] (2023). A real-time regional model for COVID-19: Probabilistic situational awareness and forecasting. PLoS Computational Biology. ISSN 1553-734X. 19(1). doi: 10.1371/journal.pcbi.1010860. Fulltekst i vitenarkiv Vis sammendrag

  The COVID-19 pandemic is challenging nations with devastating health and economic consequences. The spread of the disease has revealed major geographical heterogeneity because of regionally varying individual behaviour and mobility patterns, unequal meteorological conditions, diverse viral variants, and locally implemented non-pharmaceutical interventions and vaccination roll-out. To support national and regional authorities in surveilling and controlling the pandemic in real-time as it unfolds, we here develop a new regional mathematical and statistical model. The model, which has been in use in Norway during the first two years of the pandemic, is informed by real-time mobility estimates from mobile phone data and laboratory-confirmed case and hospitalisation incidence. To estimate regional and time-varying transmissibility, case detection probabilities, and missed imported cases, we developed a novel sequential Approximate Bayesian Computation method allowing inference in useful time, despite the high parametric dimension. We test our approach on Norway and find that three-week-ahead predictions are precise and well-calibrated, enabling policy-relevant situational awareness at a local scale. By comparing the reproduction numbers before and after lockdowns, we identify spatially heterogeneous patterns in their effect on the transmissibility, with a stronger effect in the most populated regions compared to the national reduction estimated to be 85% (95% CI 78%-89%). Our approach is the first regional changepoint stochastic metapopulation model capable of real time spatially refined surveillance and forecasting during emergencies.

• Luque, Alvaro Kohn; Myklebust, Even Moa; Tadele, Dagim Shiferaw; Giliberto, Mariaserena; Schmiester, Leonard & Noory, Jasmine [Vis alle 15 forfattere av denne artikkelen] (2023). Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data. Cell Reports Methods. 3(3). doi: 10.1016/j.crmeth.2023.100417. Fulltekst i vitenarkiv Vis sammendrag

  Tumor heterogeneity is an important driver of treatment failure in cancer since therapies often select for drug-tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug-response heterogeneity of tumor samples using traditional genomic deconvolution methods has yielded limited results, due in part to the imperfect mapping between genomic variation and functional characteristics. Here, we leverage mechanistic population modeling to develop a statistical framework for profiling phenotypic heterogeneity from standard drug-screen data on bulk tumor samples. This method, called PhenoPop, reliably identifies tumor subpopulations exhibiting differential drug responses and estimates their drug sensitivities and frequencies within the bulk population. We apply PhenoPop to synthetically generated cell populations, mixed cell-line experiments, and multiple myeloma patient samples and demonstrate how it can provide individualized predictions of tumor growth under candidate therapies. This methodology can also be applied to deconvolution problems in a variety of biological settings beyond cancer drug response.

• Parviero, Riccardo; Hellton, Kristoffer Herland; Haug, Ola; Engø-Monsen, Kenth; Rognebakke, Hanne Therese Wist & Canright, Geoffrey [Vis alle 8 forfattere av denne artikkelen] (2022). An agent-based model with social interactions for scalable probabilistic prediction of performance of a new product. International Journal of Information Management Data Insights. 2(2). doi: 10.1016/j.jjimei.2022.100127. Fulltekst i vitenarkiv Vis sammendrag

  Understanding the spreading process of new products provides valuable knowledge that can be used for effective marketing. The ability to make early prediction of success or failure is a great advantage in innovation processes. Extending current literature in a novel way, we propose a data-driven agent-based methodology that accomplishes this task. Inference and predictions are based on short-time observations of the product adoption history and knowledge of the social network of consumers. We model and predict adoptions at the agent level as driven by unobserved peer-to-peer influence and external factors such as marketing. The method compares interaction between consumers and general campaigns, and quantifies the importance of characteristics of customers and their social relations. Our computationally efficient method is demonstrated by analyzing real data, predicting the process far into the future using data from a short period after launch, and validated by simulation experiments on a true full-scale communication network.

• Page, Christian Magnus; Nøst, Therese Haugdahl; Djordjilović, Vera; Thoresen, Magne; Frigessi, Arnoldo & Sandanger, Torkjel M [Vis alle 7 forfattere av denne artikkelen] (2022). Pre-diagnostic DNA methylation in blood leucocytes in cutaneous melanoma; a nested case–control study within the Norwegian Women and Cancer cohort. Scientific Reports. 12(1). doi: 10.1038/s41598-022-18585-y. Fulltekst i vitenarkiv Vis sammendrag

  The prognosis of cutaneous melanoma depends on early detection, and good biomarkers for melanoma risk may provide a valuable tool to detect melanoma development at a pre-clinical stage. By studying the epigenetic profile in pre-diagnostic blood samples of melanoma cases and cancer free controls, we aimed to identify DNA methylation sites conferring melanoma risk. DNA methylation was measured at 775,528 CpG sites using the Illumina EPIC array in whole blood in incident melanoma cases (n = 183) and matched cancer-free controls (n = 183) in the Norwegian Women and Cancer cohort. Phenotypic information and ultraviolet radiation exposure were obtained from questionnaires. Epigenome wide association (EWAS) was analyzed in future melanoma cases and controls with conditional logistic regression, with correction for multiple testing using the false discovery rate (FDR). We extended the analysis by including a public data set on melanoma (GSE120878), and combining these different data sets using a version of covariate modulated FDR (AdaPT). The analysis on future melanoma cases and controls did not identify any genome wide significant CpG sites (0.85 ≤ padj ≤ 0.99). In the restricted AdaPT analysis, 7 CpG sites were suggestive at the FDR level of 0.15. These CpG sites may potentially be used as pre-diagnostic biomarkers of melanoma risk.

• Vitelli, Valeria; Fleischer, Thomas; Ankill, Jørgen; Arjas, Elja; Frigessi, Arnoldo & Kristensen, Vessela N. [Vis alle 7 forfattere av denne artikkelen] (2022). Transcriptomic pan-cancer analysis using rank-based Bayesian inference. Molecular Oncology. ISSN 1574-7891. 00(0), s. 1–16. doi: 10.1002/1878-0261.13354. Fulltekst i vitenarkiv Vis sammendrag

  The analysis of whole genomes of pan-cancer data sets provides a challenge for researchers, and we contribute to the literature concerning the identification of robust subgroups with clear biological interpretation. Specifically, we tackle this unsupervised problem via a novel rank-based Bayesian clustering method. The advantages of our method are the integration and quantification of all uncertainties related to both the input data and the model, the probabilistic interpretation of final results to allow straightforward assessment of the stability of clusters leading to reliable conclusions, and the transparent biological interpretation of the identified clusters since each cluster is characterized by its top-ranked genomic features. We applied our method to RNA-seq data from cancer samples from 12 tumor types from the Cancer Genome Atlas. We identified a robust clustering that mostly reflects tissue of origin but also includes pan-cancer clusters. Importantly, we identified three pan-squamous clusters composed of a mix of lung squamous cell carcinoma, head and neck squamous carcinoma, and bladder cancer, with different biological functions over-represented in the top genes that characterize the three clusters. We also found two novel subtypes of kidney cancer that show different prognosis, and we reproduced known subtypes of breast cancer. Taken together, our method allows the identification of robust and biologically meaningful clusters of pan-cancer samples.

• Eide, Simen; Leslie, David S. & Frigessi, Arnoldo (2022). Dynamic slate recommendation with gated recurrent units and Thompson sampling. Data mining and knowledge discovery. ISSN 1384-5810. 36, s. 1756–1786. doi: 10.1007/s10618-022-00849-w. Fulltekst i vitenarkiv Vis sammendrag

  We consider the problem of recommending relevant content to users of an internet platform in the form of lists of items, called slates. We introduce a variational Bayesian Recurrent Neural Net recommender system that acts on time series of interactions between the internet platform and the user, and which scales to real world industrial situations. The recommender system is tested both online on real users, and on an offline dataset collected from a Norwegian web-based marketplace, FINN.no, that is made public for research. This is one of the first publicly available datasets which includes all the slates that are presented to users as well as which items (if any) in the slates were clicked on. Such a data set allows us to move beyond the common assumption that implicitly assumes that users are considering all possible items at each interaction. Instead we build our likelihood using the items that are actually in the slate, and evaluate the strengths and weaknesses of both approaches theoretically and in experiments. We also introduce a hierarchical prior for the item parameters based on group memberships. Both item parameters and user preferences are learned probabilistically. Furthermore, we combine our model with bandit strategies to ensure learning, and introduce ‘in-slate Thompson sampling’ which makes use of the slates to maximise explorative opportunities. We show experimentally that explorative recommender strategies perform on par or above their greedy counterparts. Even without making use of exploration to learn more effectively, click rates increase simply because of improved diversity in the recommended slates.

• Azulay, Naomi; Olsen, Roy Bjørkholt; Nielsen, Christopher Sivert; Stubhaug, Audun; Jenssen, Trond Geir & Schirmer, Henrik [Vis alle 9 forfattere av denne artikkelen] (2022). Reduced heart rate variability is related to the number of metabolic syndrome components and manifest diabetes in the sixth Tromsø study 2007–2008. Scientific Reports. 12(1). doi: 10.1038/s41598-022-15824-0. Fulltekst i vitenarkiv Vis sammendrag

  Both diabetes mellitus (DM) and the metabolic syndrome (MetS) are associated with autonomic neuropathy, which predisposes to cardiac events and death. Measures of heart rate variability (HRV) can be used to monitor the activity of the autonomic nervous system (ANS), and there are strong indications that HRV can be used to study the progression of ANS-related diabetes complications. This study aims to investigate diferences in HRV in healthy, MetS and diabetic populations. Based on 7880 participants from the sixth health survey in Tromsø (Tromsø 6, 2007–2008), we found a signifcant negative association between the number of MetS components and HRV as estimated from short-term pulse wave signals (PRV). This decrease in PRV did not appear to be linear, instead it leveled of after the third component, with no signifcant diference in PRV between the MetS and DM populations. There was a signifcant negative association between HbA1c and PRV, showing a decrease in PRV occurring already within the normal HbA1c range. The MetS and DM populations are diferent from healthy controls with respect to PRV, indicating impaired ANS in both conditions. In the future, a study with assessment of PRV measurements in relation to prospective cardiovascular events seems justifed.

• Jalali, Neda; Brustad, Hilde Kjelgaard; Frigessi, Arnoldo; Macdonald, Emily Ann; Meijerink, Hinta & Feruglio, Siri Laura [Vis alle 10 forfattere av denne artikkelen] (2022). Increased household transmission and immune escape of the SARS-CoV-2 Omicron compared to Delta variants. Nature Communications. 13(1), s. 1–5. doi: 10.1038/s41467-022-33233-9. Fulltekst i vitenarkiv Vis sammendrag

  Understanding the epidemic growth of the novel SARS-CoV-2 Omicron variant is critical for public health. We compared the ten-day secondary attack rate (SAR) of the Omicron and Delta variants in households using Norwegian contact tracing data, December 2021 - January 2022. Omicron SAR was higher than Delta, with a relative risk (RR) of 1.41 (95% CI 1.27-1.56). We observed increased susceptibility to Omicron infection in household contacts compared to Delta, independent of contacts’ vaccination status. Among three-dose vaccinated contacts, the mean SAR was lower for both variants. We found increased Omicron transmissibility from primary cases to contacts in all vaccination groups, except 1-dose vaccinated, compared to Delta. Omicron SAR of three-dose vaccinated primary cases was high, 46% vs 11 % for Delta. In conclusion, three-dose vaccinated primary cases with Omicron infection can efficiently spread in households, while three-dose vaccinated contacts have a lower risk of being infected by Delta and Omicron.

• Ravindran, Vandana; Wagoner, Jessica; Athanasiadis, Paschalis; Hartigh, Andreas B Den; Sidorova, Julia & Ianevski, Aleksandr [Vis alle 11 forfattere av denne artikkelen] (2022). Discovery of host-directed modulators of virus infection by probing the SARS-CoV-2-host protein-protein interaction network. Briefings in Bioinformatics. ISSN 1467-5463. 23(6). doi: 10.1093/bib/bbac456. Fulltekst i vitenarkiv Vis sammendrag

  The ongoing coronavirus disease 2019 (COVID-19) pandemic has highlighted the need to better understand virus–host interactions. We developed a network-based method that expands the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)–host protein interaction network and identifies host targets that modulate viral infection. To disrupt the SARS-CoV-2 interactome, we systematically probed for potent compounds that selectively target the identified host proteins with high expression in cells relevant to COVID-19. We experimentally tested seven chemical inhibitors of the identified host proteins for modulation of SARS-CoV-2 infection in human cells that express ACE2 and TMPRSS2. Inhibition of the epigenetic regulators bromodomain-containing protein 4 (BRD4) and histone deacetylase 2 (HDAC2), along with ubiquitin-specific peptidase (USP10), enhanced SARS-CoV-2 infection. Such proviral effect was observed upon treatment with compounds JQ1, vorinostat, romidepsin and spautin-1, when measured by cytopathic effect and validated by viral RNA assays, suggesting that the host proteins HDAC2, BRD4 and USP10 have antiviral functions. We observed marked differences in antiviral effects across cell lines, which may have consequences for identification of selective modulators of viral infection or potential antiviral therapeutics. While network-based approaches enable systematic identification of host targets and selective compounds that may modulate the SARS-CoV-2 interactome, further developments are warranted to increase their accuracy and cell-context specificity.

• Pesonen, Henri; Simola, Umberto; Luque, Alvaro Kohn; Vuollekoski, Henri; Lai, Xiaoran & Frigessi, Arnoldo [Vis alle 11 forfattere av denne artikkelen] (2022). ABC of the future. International Statistical Review. ISSN 0306-7734. doi: 10.1111/insr.12522. Fulltekst i vitenarkiv Vis sammendrag

  Approximate Bayesian computation (ABC) has advanced in two decades from a seminal idea to a practically applicable inference tool for simulator-based statistical models, which are becoming increasingly popular in many research domains. The computational feasibility of ABC for practical applications has been recently boosted by adopting techniques from machine learning to build surrogate models for the approximate likelihood or posterior and by the introduction of a general-purpose software platform with several advanced features, including automated parallelisation. Here we demonstrate the strengths of the advances in ABC by going beyond the typical benchmark examples and considering real applications in astronomy, infectious disease epidemiology, personalised cancer therapy and financial prediction. We anticipate that the emerging success of ABC in producing actual added value and quantitative insights in the real world will continue to inspire a plethora of further applications across different fields of science, social science and technology.

• Lai, Xiaoran; Taskén, Håkon Austlid; Mo, Torgeir; Funke, Simon Wolfgang; Frigessi, Arnoldo & Rognes, Marie Elisabeth [Vis alle 7 forfattere av denne artikkelen] (2021). A scalable solver for a stochastic, hybrid cellular automaton model of personalized breast cancer therapy. International Journal for Numerical Methods in Biomedical Engineering. ISSN 2040-7939. 38(1). doi: 10.1002/cnm.3542. Fulltekst i vitenarkiv Vis sammendrag

  Mathematical modeling and simulation is a promising approach to personalizedcancer medicine. Yet, the complexity, heterogeneity and multi-scale nature ofcancer pose significant computational challenges. Coupling discrete cell-basedmodels with continuous models using hybrid cellular automata (CA) is a power-ful approach for mimicking biological complexity and describing the dynamicalexchange of information across different scales. However, when clinically rele-vant cancer portions are taken into account, such models become computation-ally very expensive. While efficient parallelization techniques for continuousmodels exist, their coupling with discrete models, particularly CA, necessitatesmore elaborate solutions. Building upon FEniCS, a popular and powerful scien-tific computing platform for solving partial differential equations, we developedparallel algorithms to link stochastic CA with differential equations (https://bitbucket.org/HTasken/cansim). The algorithms minimize the communicationbetween processes that share CA neighborhood values while also allowing forreproducibility during stochastic updates. We demonstrated the potential of oursolution on a complex hybrid cellular automaton model of breast cancer treatedwith combination chemotherapy. On a single-core processor, we obtained nearlylinear scaling with an increasing problem size, whereas weak parallel scalingshowed moderate growth in solving time relative to increase in problem size.Finally, we applied the algorithm to a problem that is 500 times larger than previ-ous work, allowing us to run personalized therapy simulations based on hetero-geneous cell density and tumor perfusion conditions estimated from magneticresonance imaging data on an unprecedented scale

• Lindstrøm, Jonas Christoffer; Engebretsen, Solveig; Kristoffersen, Anja Bråthen; Rø, Gunnar Øyvind Isaksson; Palomares, Alfonso Diz-Lois & Engø-Monsen, Kenth [Vis alle 14 forfattere av denne artikkelen] (2021). Increased transmissibility of the alpha SARS-CoV-2 variant: evidence from contact tracing data in Oslo, January to February 2021. Infectious Diseases. ISSN 2374-4235. 54(1), s. 72–77. doi: 10.1080/23744235.2021.1977382. Fulltekst i vitenarkiv Vis sammendrag

  Background: Information about the contagiousness of new SARS-CoV-2 variants, including the alpha lineage, and how they spread in various locations is essential. Country-specific estimates are needed because local interventions influence transmissibility. Methods: We analysed contact tracing data from Oslo municipality, reported from January through February 2021, when the alpha lineage became predominant in Norway and estimated the relative transmissibility of the alpha lineage with the use of Poisson regression. Results: Within households, we found an increase in the secondary attack rate by 60% (95% CI 20–114%) among cases infected with the alpha lineage compared to other variants; including all close contacts, the relative increase in the secondary attack rate was 24% (95% CI −6%−43%). There was a significantly higher risk of infecting household members in index cases aged 40–59 years who were infected with the alpha lineage; we found no association between transmission and household size. Overall, including all close contacts, we found that the reproduction number among cases with the alpha lineage was increased by 24% (95% CI 0%−52%), corresponding to an absolute increase of 0.19, compared to the group of index cases infected with other variants. Conclusion: Our study suggests that households are the primary locations for rapid transmission of the new lineage alpha.

• Eide, Simen; Leslie, David; Frigessi, Arnoldo; Rishaug, Joakim; Jenssen, Helge & Verrewaere, Sofie (2021). FINN. no Slates Dataset: A new Sequential Dataset Logging Interactions, all Viewed Items and Click Responses/No-Click for Recommender Systems Research. I Corona, Humberto; Larson, Martha & Willemsen, Martijn C. (Red.), RecSys '21 : Fifteenth ACM Conference on Recommender Systems. Association for Computing Machinery (ACM). ISSN 9781450384582. doi: 10.1145/3460231.3474607. Fulltekst i vitenarkiv
• Divino, Fabio; Belay, Denekew Bitew; Keilman, Nico & Frigessi, Arnoldo (2021). Spatial modelling of Lexis mortality data. Spatial Statistics. 44. doi: 10.1016/j.spasta.2021.100532. Fulltekst i vitenarkiv Vis sammendrag

  We present a spatial approach to investigate mortality data referenced over a Lexis structure. We decompose the force of mortality into two interpretable components: a Markov random field, homogeneous with respect to age, period and cohort which explains the main pattern of mortality; and a secondary component of independent shocks, accounting for additional non structured patterns. Inference is based on a hierarchical Bayesian model with Markov chain Monte Carlo computations. We present an application to data from the Human Mortality Database, with respect to Italy and Sweden, two countries with very different histories in terms of demographic and epidemiological transitions. For each country the primary surface of spatially structured mortality and the secondary surface of additional mortality are estimated. The importance of each component is valuated by the estimated value of the respective precision parameter. In both Italy and Sweden, we discovered an interesting band of extra mortality in the secondary surface across the time domain, in the age interval between 60 and 90 years, with a slightly positive slope. The band represents a significant amount of extra mortality for the elderly population, which is otherwise incompatible with a structured dynamics in age, period and cohort.

• Engebretsen, Solveig; Engø-Monsen, Kenth; Aleem, Mohammad Abdul; Gurley, Emily Suzanne; Rattalma, Arnoldo Frigessi Di & Blasio, Birgitte Freiesleben de (2020). Time-aggregated mobile phone mobility data are sufficient for modelling influenza spread: the case of Bangladesh. Journal of the Royal Society Interface. ISSN 1742-5689. 17(167). doi: 10.1098/rsif.2019.0809. Fulltekst i vitenarkiv Vis sammendrag

  Human mobility plays a major role in the spatial dissemination of infectious diseases. We develop a spatio-temporal stochastic model for influenza-like disease spread based on estimates of human mobility. The model is informed by mobile phone mobility data collected in Bangladesh. We compare predictions of models informed by daily mobility data (reference) with that of models informed by time-averaged mobility data, and mobility model approximations. We find that the gravity model overestimates the spatial synchrony, while the radiation model underestimates the spatial synchrony. Using time-averaged mobility resulted in spatial spreading patterns comparable to the daily mobility model. We fit the model to 2014–2017 influenza data from sentinel hospitals in Bangladesh, using a sequential version of approximate Bayesian computation. We find a good agreement between our estimated model and the case data. We estimate transmissibility and regional spread of influenza in Bangladesh, which are useful for policy planning. Time-averaged mobility appears to be a good proxy for human mobility when modelling infectious diseases. This motivates a more general use of the time-averaged mobility, with important implications for future studies and outbreak control. Moreover, time-averaged mobility is subject to less privacy concerns than daily mobility, containing less temporal information on individual movements

• Pladsen, Arne Valebjørg; Nilsen, Gro; Rueda, Oscar M.; Aure, Miriam Ragle; Borgan, Ørnulf & Liestøl, Knut [Vis alle 18 forfattere av denne artikkelen] (2020). DNA copy number motifs are strong and independent predictors of survival in breast cancer. Communications Biology. 3. doi: 10.1038/s42003-020-0884-6. Fulltekst i vitenarkiv Vis sammendrag

  Somatic copy number alterations are a frequent sign of genome instability in cancer. A precise characterization of the genome architecture would reveal underlying instability mechanisms and provide an instrument for outcome prediction and treatment guidance. Here we show that the local spatial behavior of copy number profiles conveys important information about this architecture. Six filters were defined to characterize regional traits in copy number profiles, and the resulting Copy Aberration Regional Mapping Analysis (CARMA) algorithm was applied to tumors in four breast cancer cohorts (n = 2919). The derived motifs represent a layer of information that complements established molecular classifications of breast cancer. A score reflecting presence or absence of motifs provided a highly significant independent prognostic predictor. Results were consistent between cohorts. The nonsite-specific occurrence of the detected patterns suggests that CARMA captures underlying replication and repair defects and could have a future potential in treatment stratification.

• Page, Christian; Djordjilovic, Vera; Nøst, Therese Haugdahl; Ghiasvand, Reza; Sandanger, Torkjel M & Rattalma, Arnoldo Frigessi Di [Vis alle 8 forfattere av denne artikkelen] (2020). Lifetime ultraviolet radiation exposure and DNA methylation in blood leukocytes: The Norwegian Women and Cancer Study. Scientific Reports. 10, s. 1–8. doi: 10.1038/s41598-020-61430-3. Fulltekst i vitenarkiv Vis sammendrag

  Ultraviolet radiation (UVR) exposure is a leading cause of skin cancers and an ubiquitous environmental exposure. However, the molecular mechanisms relating UVR exposure to melanoma is not fully understood. We aimed to investigate if lifetime UVR exposure could be robustly associated to DNA methylation (DNAm). We assessed DNAm in whole blood in three data sets (n = 183, 191, and 125) from the Norwegian Woman and Cancer cohort, using Illumina platforms. We studied genome-wide DNAm, targeted analyses of CpG sites indicated in the literature, global methylation, and accelerated aging. Lifetime history of UVR exposure (residential ambient UVR, sunburns, sunbathing vacations and indoor tanning) was collected by questionnaires. We used one data set for discovery and the other two for replication. One CpG site showed a genome-wide significant association to cumulative UVR exposure (cg01884057) (pnominal = 3.96e-08), but was not replicated in any of the two replication sets (pnominal ≥ 0.42). Two CpG sites (cg05860019, cg00033666) showed suggestive associations with the other UVR exposures. We performed extensive analyses of the association between long-term UVR exposure and DNAm. There was no indication of a robust effect of past UVR exposure on DNAm.

• Bergholtz, Helga; Lien, Tonje Gulbrandsen; Swanson, David; Rattalma, Arnoldo Frigessi Di; Daidone, Maria Grazia & Tost, Jörg [Vis alle 8 forfattere av denne artikkelen] (2020). Contrasting DCIS and invasive breast cancer by subtype suggests basal-like DCIS as distinct lesions. NPJ breast cancer. 6. doi: 10.1038/s41523-020-0167-x. Fulltekst i vitenarkiv Vis sammendrag

  Ductal carcinoma in situ (DCIS) is a non-invasive type of breast cancer with highly variable potential of becoming invasive and affecting mortality. Currently, many patients with DCIS are overtreated due to the lack of specific biomarkers that distinguish low risk lesions from those with a higher risk of progression. In this study, we analyzed 57 pure DCIS and 313 invasive breast cancers (IBC) from different patients. Three levels of genomic data were obtained; gene expression, DNA methylation, and DNA copy number. We performed subtype stratified analyses and identified key differences between DCIS and IBC that suggest subtype specific progression. Prominent differences were found in tumors of the basal-like subtype: Basal-like DCIS were less proliferative and showed a higher degree of differentiation than basal-like IBC. Also, core basal tumors (characterized by high correlation to the basal-like centroid) were not identified amongst DCIS as opposed to IBC. At the copy number level, basal-like DCIS exhibited fewer copy number aberrations compared with basal-like IBC. An intriguing finding through analysis of the methylome was hypermethylation of multiple protocadherin genes in basal-like IBC compared with basal-like DCIS and normal tissue, possibly caused by long range epigenetic silencing. This points to silencing of cell adhesion-related genes specifically in IBC of the basal-like subtype. Our work confirms that subtype stratification is essential when studying progression from DCIS to IBC, and we provide evidence that basal-like DCIS show less aggressive characteristics and question the assumption that basal-like DCIS is a direct precursor of basal-like invasive breast cancer.

• Crispino, Marta; Arjas, Elja; Vitelli, Valeria; Barrett, Natasha & Rattalma, Arnoldo Frigessi Di (2019). A Bayesian Mallows approach to nontransitive pair comparison data: How human are sounds? Annals of Applied Statistics. ISSN 1932-6157. 13(1), s. 492–519. doi: 10.1214/18-AOAS1203. Fulltekst i vitenarkiv Vis sammendrag

  We are interested in learning how listeners perceive sounds as having human origins. An experiment was performed with a series of electronically synthesized sounds, and listeners were asked to compare them in pairs. We propose a Bayesian probabilistic method to learn individual preferences from nontransitive pairwise comparison data, as happens when one (or more) individual preferences in the data contradicts what is implied by the others. We build a Bayesian Mallows model in order to handle nontransitive data, with a latent layer of uncertainty which captures the generation of preference misreporting. We then develop a mixture extension of the Mallows model, able to learn individual preferences in a heterogeneous population. The results of our analysis of the musicology experiment are of interest to electroacoustic composers and sound designers, and to the audio industry in general, whose aim is to understand how computer generated sounds can be produced in order to sound more human.

• Tekpli, Xavier; Lien, Tonje Gulbrandsen; Røssevold, Andreas Hagen; Nebdal, Daniel J.H.; Borgen, Elin & Ohnstad, Hege Oma [Vis alle 19 forfattere av denne artikkelen] (2019). An independent poor-prognosis subtype of breast cancer defined by a distinct tumor immune microenvironment. Nature Communications. 10:5499, s. 1–14. doi: 10.1038/s41467-019-13329-5. Fulltekst i vitenarkiv Vis sammendrag

  How mixtures of immune cells associate with cancer cell phenotype and affect pathogenesis is still unclear. In 15 breast cancer gene expression datasets, we invariably identify three clusters of patients with gradual levels of immune infiltration. The intermediate immune infiltration cluster (Cluster B) is associated with a worse prognosis independently of known clinicopathological features. Furthermore, immune clusters are associated with response to neoadjuvant chemotherapy. In silico dissection of the immune contexture of the clusters identified Cluster A as immune cold, Cluster C as immune hot while Cluster B has a pro-tumorigenic immune infiltration. Through phenotypical analysis, we find epithelial mesenchymal transition and proliferation associated with the immune clusters and mutually exclusive in breast cancers. Here, we describe immune clusters which improve the prognostic accuracy of immune contexture in breast cancer. Our discovery of a novel independent prognostic factor in breast cancer highlights a correlation between tumor phenotype and immune contexture.

• Lai, Xiaoran; Geier, Oliver; Fleischer, Thomas; Garred, Øystein; Borgen, Elin & Funke, Simon Wolfgang [Vis alle 14 forfattere av denne artikkelen] (2019). Towards personalized computer simulation of breast cancer treatment: a multi-scale pharmacokinetic and pharmacodynamic model informed by multi-type patient data. Cancer Research. ISSN 0008-5472. 79(16), s. 4293–4304. doi: 10.1158/0008-5472.CAN-18-1804. Fulltekst i vitenarkiv Vis sammendrag

  The usefulness of mechanistic models to disentangle complex multiscale cancer processes, such as treatment response, has been widely acknowledged. However, a major barrier for multiscale models to predict treatment outcomes in individual patients lies in their initialization and parametrization, which needs to reflect individual cancer characteristics accurately. In this study, we use multitype measurements acquired routinely on a single breast tumor, including histopathology, MRI, and molecular profiling, to personalize parts of a complex multiscale model of breast cancer treated with chemotherapeutic and antiangiogenic agents. The model accounts for drug pharmacokinetics and pharmacodynamics. We developed an open-source computer program that simulates cross-sections of tumors under 12-week therapy regimens and used it to individually reproduce and elucidate treatment outcomes of 4 patients. Two of the tumors did not respond to therapy, and model simulations were used to suggest alternative regimens with improved outcomes dependent on the tumor's individual characteristics. It was determined that more frequent and lower doses of chemotherapy reduce tumor burden in a low proliferative tumor while lower doses of antiangiogenic agents improve drug penetration in a poorly perfused tumor. Furthermore, using this model, we were able to correctly predict the outcome in another patient after 12 weeks of treatment. In summary, our model bridges multitype clinical data to shed light on individual treatment outcomes.

Se alle arbeider i NVA

• Eskeland, Gunnar; Glad, Ingrid Kristine & Frigessi, Arnoldo (2026). Europa må regulere KI | DN. Fulltekst i vitenarkiv Vis sammendrag

  Høvdingene innen kunstig intelligens (KI) babler om trusler mot menneskeheten og ber om regulering, og da kreves skepsis i responsen (jf. Mogstad og Hardings kronikk i DN 2. oktober: «Hvorfor innovere når man kan regulere?»).Det de ber om, skal de ikke få.Ikke i Europa.

• Zuccato, Lorenzo; George, Anne-Marie; Vitelli, Valeria; Frigessi, Arnoldo; Giese, Martin & Scheel, Ida (2026). Bridging Statistical and Logical Perspectives on Inconsistency. Fulltekst i vitenarkiv
• Haff, Ingrid Hobæk; Griesbauer, Elisabeth Maria; Czado, Claudia & Frigessi, Arnoldo (2026). Synthetic data with vine copulas – balancing utility and privacy. Fulltekst i vitenarkiv
• Glad, Ingrid Kristine & Frigessi, Arnoldo (2025). KI = Statistikk2.0 ? Fulltekst i vitenarkiv Vis sammendrag

  Foredrag i anledning den internasjonale statistikk-dagen

• Frigessi, Arnoldo (2025). Integrering av kunstig intelligens i pasientbehandling. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2025). True data and their synthetic versions. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2025). True data and their synthetic versions. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2025). What can we expect from AI in the next 5 years? Fulltekst i vitenarkiv
• Griesbauer, Elisabeth Maria; Czado, Claudia; Frigessi, Arnoldo & Haff, Ingrid Hobæk (2025). Synthetic data generation balancing privacy and utility, using vine copulas. Fulltekst i vitenarkiv Vis sammendrag

  The availability of high-quality data has led to tremendous advances in science, technology and society at large, when analysed by means of statistical and machine learning (ML) methods. However, real-world data in many cases cannot be made public to the research community due to privacy restrictions. This impairs progress especially in bio-medical research. Synthetic data can substitute the sensitive real data, as long as they do not disclose private aspects. This has proven to be successful in training downstream ML applications. We propose TVineSynth, a vine copula based synthetic tabular data generator. TVineSynth is designed to balance privacy and utility, using the vine tree structure and its truncation to do the trade-off. Contrary to synthetic data generators that achieve differential privacy (DP) by globally adding noise, TVineSynth performs a controlled approximation of the estimated data generating distribution. Because of this it does not suffer from poor utility of the resulting synthetic data for downstream prediction tasks. TVineSynth introduces a targeted bias into the vine copula model. Combined with the specific tree structure of the vine, this causes the model to zero out privacy-leaking dependencies while relying on those that are beneficial for utility. We theoretically justify how the construction of TVineSynth ensures privacy. When compared to competitor models, with and without DP, TVineSynth achieves a superior privacy-utility balance.

• Griesbauer, Elisabeth Maria; Czado, Claudia; Frigessi, Arnoldo & Haff, Ingrid Hobæk (2025). Poster: TVineSynth: A Truncated C-Vine Copula Generator of Synthetic Tabular Data to Balance Privacy and Utility. Fulltekst i vitenarkiv Vis sammendrag

  We propose TVineSynth, a vine copula based synthetic tabular data generator, which is designed to balance privacy and utility, using the vine tree structure and its truncation to do the trade-off. Contrary to synthetic data generators that achieve DP by globally adding noise, TVineSynth performs a controlled approximation of the estimated data generating distribution, so that it does not suffer from poor utility of the resulting synthetic data for downstream prediction tasks. TVineSynth introduces a targeted bias into the vine copula model that, combined with the specific tree structure of the vine, causes the model to zero out privacy-leaking dependencies while relying on those that are beneficial for utility. Privacy is here measured with membership (MIA) and attribute inference attacks (AIA). Further, we theoretically justify how the construction of TVineSynth ensures AIA privacy under a natural privacy measure for continuous sensitive attributes. When compared to competitor models, with and without DP, on simulated and on real-world data, TVineSynth achieves a superior privacy-utility balance.

• Griesbauer, Elisabeth Maria; Czado, Claudia; Frigessi, Arnoldo & Haff, Ingrid Hobæk (2025). Poster: TVineSynth: A Truncated C-Vine Copula Generator of Synthetic Tabular Data to Balance Privacy and Utility. Fulltekst i vitenarkiv Vis sammendrag

  We propose TVineSynth, a vine copula based synthetic tabular data generator, which is designed to balance privacy and utility, using the vine tree structure and its truncation to do the trade-off. Contrary to synthetic data generators that achieve DP by globally adding noise, TVineSynth performs a controlled approximation of the estimated data generating distribution, so that it does not suffer from poor utility of the resulting synthetic data for downstream prediction tasks. TVineSynth introduces a targeted bias into the vine copula model that, combined with the specific tree structure of the vine, causes the model to zero out privacy-leaking dependencies while relying on those that are beneficial for utility. Privacy is here measured with membership (MIA) and attribute inference attacks (AIA). Further, we theoretically justify how the construction of TVineSynth ensures AIA privacy under a natural privacy measure for continuous sensitive attributes. When compared to competitor models, with and without DP, on simulated and on real-world data, TVineSynth achieves a superior privacy-utility balance.

• Glad, Ingrid Kristine & Frigessi, Arnoldo (2025). Nysgjerrige på: neste generasjon maskinlæring - Nysgjerrige Norge | Acast. Fulltekst i vitenarkiv Vis sammendrag

  Listen to Nysgjerrige på: neste generasjon maskinlæring from Nysgjerrige Norge. Nysgjerrige Norge - Episode 3: Et dypdykk i kunnskapsdrevet maskinlæring med IntegreatI denne episoden tar Kristopher turen til Nils Henrik Abels hus på Blindern i Oslo for å besøke Integreat Senter for Fremragende Forskning. Vi møter professorene og ekteparet Arnoldo Frigessi og Ingrid Glad, som leder det nyoppstartede senteret. De deler visjoner om fremtidens algoritmer, hvor kunnskap og etikk spiller en nøkkelrolle i utviklingen av kunstig intelligens (KI).Nysgjerrige Norge er en serie fra Norges forskningsråd. Sentre for Fremragende Forskning er en støtteordning til landets fremste vitenskapelige miljøer. Du kan lese mer om støtteordningen her. Serien er produsert av Moose Media. Programleder: Kristopher Schau. Musikk: The Dogs. Abonnér på Nysgjerrige Norge og møt flere av landets fremste vitenskapspersonligheter.

• Storvik, Geir Olve; Engebretsen, Solveig; Blasio, Birgitte Freiesleben De & Frigessi, Arnoldo (2025). Flaws in the Article “Nearly Instantaneous Time-Varying Reproduction Number for Contagious Diseases—a Direct Approach Based on Nonlinear Regression". Journal of Computational Biology. ISSN 1066-5277. 32(8), s. 813–818. doi: https:/www.liebertpub.com/doi/full/10.1177/15578666251360613. Fulltekst i vitenarkiv
• Engebretsen, Solveig; Thorarinsdottir, Thordis Linda; Palomares, Alfonso Diz-Lois; Storvik, Geir Olve; Frigessi, Arnoldo & Blasio, Birgitte Freiesleben De (2025). Contribution to the Discussion of 'Some statistical aspects of the Covid-19 response' by Wood et al. Journal of the Royal Statistical Society, Series A (Statistics in Society). ISSN 0964-1998. 189(1), s. 71–72. doi: 10.1093/jrsssa/qnaf080. Fulltekst i vitenarkiv
• Griesbauer, Elisabeth Maria; Czado, Claudia; Frigessi, Arnoldo & Haff, Ingrid Hobæk (2024). Synthetic data generation balancing privacy and utility, using vine copulas. doi: https:/nva.sikt.no/registration/0199812d9067-f1497889-e36d-4995-8686-7c68d1e99cdb/edit. Fulltekst i vitenarkiv Vis sammendrag

  The availability of diverse, high-quality data has led to tremendous advances in science, technology and society at large, when analysed by means of statistical and machine learning (ML) methods. However, real-world data, in many cases, cannot be made public to the research community due to privacy restrictions, obstructing progress, especially in bio- medical research. Synthetic data can substitute the sensitive real data, and as long as they do not disclose private aspects. This has proven to be successful in training downstream ML applications. We propose TVineSynth, a vine copula based synthetic tabular data generator, which is designed to balance privacy and utility, using the vine tree structure and its truncation to do the trade-off. Contrary to synthetic data generators that achieve differential privacy (DP) by globally adding noise, TVineSynth performs a controlled approximation of the estimated data generating distribution, so that it does not suffer from poor utility of the resulting synthetic data for downstream prediction tasks. TVineSynth introduces a targeted bias into the vine copula model that, combined with the specific tree structure of the vine, causes the model to zero out privacy-leaking dependencies while relying on those that are beneficial for utility. Privacy is here measured with membership (MIA) and attribute inference attacks (AIA). Further, we theoretically justify how the construction of TVineSynth ensures AIA privacy under a natural privacy measure for continuous sensitive attributes. When compa red to competitor models, with and without DP, on simulated and on real-world data, TVineSynth achieves a superior privacy-utility balance.

• Frigessi, Arnoldo (2024). Om kunstig intelligens - NRK Nyhetsmorgen (2. januar 2024). [TV]. NRK. Fulltekst i vitenarkiv
• Haff, Ingrid Hobæk; Griesbauer, Elisabeth Maria; Frigessi, Arnoldo & Czado, Claudia (2024). Synthetic data with vine copulas – balancing utility and privacy. Fulltekst i vitenarkiv
• Haff, Ingrid Hobæk; Griesbauer, Elisabeth Maria; Frigessi, Arnoldo & Czado, Claudia (2024). Synthetic data with vine copulas – balancing utility and privacy. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2024). Supporting the AI revolution. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2024). Kunstig intelligens for anestesiforskning og praksis. Fulltekst i vitenarkiv
• Zucknick, Manuela; Frigessi, Arnoldo & Smajdor, Anna (2024). Autonomy and decision-making in the era of medical AI: the red pill or the blue? Fulltekst i vitenarkiv
• Pérez, Tomás Fernando Varnet; Øvergaard, Kristin Romvig; Frigessi, Arnoldo & Biele, Guido (2024). Design-aware imputation in a target trial of ADHD pharmaceutical treatment on Norwegian national test scores: the case of simultaneous outcomes. Fulltekst i vitenarkiv
• Cavalieri, Stefano; Vener, Claudia; Leblanc, Marissa Erin; Lopez-Perez, Laura; Fico, Giuseppe & Resteghini, Carlo [Vis alle 23 forfattere av denne artikkelen] (2023). A multicenter randomized trial for quality of life evaluation by non-invasive intelligent tools during post-curative treatment follow-up for head and neck cancer: Clinical study protocol. Frontiers in Oncology. 13, s. 1–16. doi: 10.3389/fonc.2023.1048593. Fulltekst i vitenarkiv Vis sammendrag

  Patients surviving head and neck cancer (HNC) suffer from high physical, psychological, and socioeconomic burdens. Achieving cancer-free survival with an optimal quality of life (QoL) is the primary goal for HNC patient management. So, maintaining lifelong surveillance is critical. An ambitious goal would be to carry this out through the advanced analysis of environmental, emotional, and behavioral data unobtrusively collected from mobile devices. The aim of this clinical trial is to reduce, with non-invasive tools (i.e., patients’ mobile devices), the proportion of HNC survivors (i.e., having completed their curative treatment from 3 months to 10 years) experiencing a clinically relevant reduction in QoL during follow-up. The Big Data for Quality of Life (BD4QoL) study is an international, multicenter, randomized (2:1), open-label trial. The primary endpoint is a clinically relevant global health-related EORTC QLQ-C30 QoL deterioration (decrease ≥10 points) at any point during 24 months post-treatment follow-up. The target sample size is 420 patients. Patients will be randomized to be followed up using the BD4QoL platform or per standard clinical practice. The BD4QoL platform includes a set of services to allow patients monitoring and empowerment through two main tools: a mobile application installed on participants’ smartphones, that includes a chatbot for e-coaching, and the Point of Care dashboard, to let the investigators manage patients data. In both arms, participants will be asked to complete QoL questionnaires at study entry and once every 6 months, and will undergo post-treatment follow up as per clinical practice. Patients randomized to the intervention arm (n=280) will receive access to the BD4QoL platform, those in the control arm (n=140) will not. Eligibility criteria include completing curative treatments for non-metastatic HNC and the use of an Android-based smartphone. Patients undergoing active treatments or with synchronous cancers are excluded.

• Frigessi, Arnoldo (2023). Hjernen i sentrum: Kunstig intelligens Hva er best; kunstig eller ekte intelligens? Fulltekst i vitenarkiv
• Haff, Ingrid Hobæk; Griesbauer, Elisabeth Maria; Frigessi, Arnoldo & Czado, Claudia (2023). Synthetic data with vine-copulas - balancing utility and privacy. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2023). From limited patient data, to high frequency synthetic data, to the differential equation of a breast tumour growth. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2023). The AI algorithm that you are using has its own agenda: you might want to know it. Fulltekst i vitenarkiv
• Frigessi, Arnoldo; Smajdor, Anna & Zucknick, Manuela (2023). The Importance of Reward Functions in Machine Learning Algorithms and Why These Should be Regulated. Fulltekst i vitenarkiv Vis sammendrag

  Session III: AI in Practice Presentation: The Importance of Reward Functions in Machine Learning Algorithms and Why These Should be Regulated presented by Frigessi

• Frigessi, Arnoldo (2023). Pair copula constructions in machine learning. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2023). Kunstig intelligens, algoritmer i klinisk praksis, hvor er vi, hva skjer nå, muligheter og utfordringer. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2023). The AI ecosystem. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2023). AI i medisin og klinikk. Fulltekst i vitenarkiv
• Pérez, Tomás Fernando Varnet; Biele, Guido; Frigessi, Arnoldo & Øvergaard, Kristin Romvig (2023). Synthetic data for unidentified Bayesian multiple bias models an application to ADHD interventions for academic achievement. Fulltekst i vitenarkiv
• Andreassen, Tor Arne; Frigessi, Arnoldo & Spurkland, Anne (2022). Omikron vil neppe smitte alle. Men en eller annen variant, vil nesten alle bli smittet av. [Journal]. Aftenposten. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2022). Probabilistic preference learning from incomplete rank data. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2022). A Bayesian two-way latent structure clustering model for multiple data integration, with an application to subtyping breast cancer. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2022). A Bayesian two-way latent structure clustering model for multiple data integration, with an application to subtyping breast cancer. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2021). Data Science for the covid-19 emergency. Fulltekst i vitenarkiv
• Stoltenberg, Camilla; Aavitsland, Preben; Rø, Gunnar Øyvind Isaksson; Blasio, Birgitte Freiesleben de; Rattalma, Arnoldo Frigessi Di & Engebretsen, Solveig (2021). Aftenposten bommer om prognoser. Igjen og igjen. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2021). Mathematical modelling of the Sars-CoV-2 pandemic in Norway. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2021). Data science in an emergency. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2021). Models and predictions during the Covid-19 emergency in Norway. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2021). Sverdrup prisen 2021. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2021). Data science in emergency. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2021). The Norwegian regional stochastic model of the Covid-19 epidemics. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2021). Math&Stats modelling of breast cancer therapy. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2021). Covid-19 modelling and AI. Fulltekst i vitenarkiv
• Frigessi, Arnoldo (2021). Debatt i NRK radio P2 om utnyttelse av norske helsedata. [Radio]. NRK P2. Fulltekst i vitenarkiv
• Stoltenberg, Camilla; Rattalma, Arnoldo Frigessi Di; Engebretsen, Solveig; Sandberg, Hallvard; Johansen, Per Anders & Gjuvsland, Elin Ruhlin (2021). Paneldebatt: HVA KAN VI LÆRE AV RUNDEN MED COVID: HVORDAN HÅNDTERER VI USIKKERHET OG ÅPENHET? [Internet]. Arendalsuka. Fulltekst i vitenarkiv
• Løland, Anders & Rattalma, Arnoldo Frigessi Di (2020). Episode 2: Om den essensielle usikkerheten. Med Arnoldo Frigessi. [Internet]. Sannsynligvis VIKTIG (podkast). Fulltekst i vitenarkiv
• Løland, Anders & Rattalma, Arnoldo Frigessi Di (2020). Episode 3: Hvorfor skal forskning være en del av beredskapen vår? Med Arnoldo Frigessi. [Internet]. Sannsynligvis VIKTIG (podkast). Fulltekst i vitenarkiv
• Rattalma, Arnoldo Frigessi Di (2020). The Norwegian Covid-19 Model: a spatial modelling of the early phase epidemics. Fulltekst i vitenarkiv
• Rattalma, Arnoldo Frigessi Di (2020). Spatial modelling of early-phase COVID-19 epidemic in Norway. Fulltekst i vitenarkiv
• Rattalma, Arnoldo Frigessi Di (2020). In silico modelling of breast tumours for personalised therapy. Fulltekst i vitenarkiv
• Rattalma, Arnoldo Frigessi Di; Köhn-Luque, Alvaro; Kristensen, Vessela N.; Gørbitz, Carl Henrik & Engebråten, Olav (2020). Kan statistikk og matematikk redde like mange liv som nye medisiner? Aftenposten (morgenutg. : trykt utg.). ISSN 0804-3116. Fulltekst i vitenarkiv
• Krogstie, John; Storsul, Tanja; Frigessi, Arnoldo; Lyngstad, Cathrine Pihl; Andreassen, Eirik & Martinkenaite, Ieva [Vis alle 13 forfattere av denne artikkelen] (2023). Overordnede tiltak - Andre notat fra rådgivende ekspertgruppe for KI-satsingen. Norges forskningsråd. Fulltekst i vitenarkiv
• Storsul, Tanja; Rettberg, Jill Walker; Brandtzæg, Petter Bae; Frigessi, Arnoldo; Strumke, Inga & Mahler, Tobias [Vis alle 12 forfattere av denne artikkelen] (2023). 1. notat fra rådgivende ekspertgruppe for KI-satsingen: Nåsituasjon og forventninger. Forskningsrådet. Fulltekst i vitenarkiv
• Blasio, Birgitte Freiesleben de; Ruscio, Francesco Di; Rø, Gunnar Øyvind Isaksson; Engebretsen, Solveig; Frigessi, Arnoldo & Palomares, Alfonso Diz-Lois [Vis alle 14 forfattere av denne artikkelen] (2020). Situational awareness and forecasting - FHI COVID-19 modelling team. Folkehelseinstituttet. Fulltekst i vitenarkiv

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