Arnoldo Frigessi

• Akman, Tuğba; 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(4). doi: 10.1007/s11538-023-01253-1.
• 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.
• Chan, Yat Hin; Rø, Gunnar; Midtbø, Jørgen E.; Di Ruscio, Francesco; 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.
• 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
• Kohn Luque, Alvaro; 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
• Kamineni, Meghana; Engø-Monsen, Kenth; Midtbø, Jørgen E.; Forland, Frode; De Blasio, Birgitte Freiesleben & 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.

• 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.

• Storvik, Geir Olve; Diz-Lois Palomares, Alfonso; 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
• 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
• 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
• Pesonen, Henri; Simola, Umberto; Kohn Luque, Alvaro; 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
• 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.

• Azulay, Naomi; Bjørkholt Olsen, Roy; 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. ISSN 2045-2322. 12. doi: 10.1038/s41598-022-15824-0. Fulltekst i vitenarkiv
• 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. ISSN 2041-1723. 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.

• 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. ISSN 2045-2322. 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.

• 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.

• 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.
• Divino, Fabio; Belay, Denekew Bitew; Keilman, Nico & Frigessi, Arnoldo (2021). Spatial modelling of Lexis mortality data. Spatial Statistics. ISSN 2211-6753. 44. doi: 10.1016/j.spasta.2021.100532. 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.

• Lindstrøm, Jonas Christoffer; Engebretsen, Solveig; Kristoffersen, Anja Bråthen; Rø, Gunnar Øyvind Isaksson; Diz-Lois Palomares, Alfonso & 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.

• 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

• Engebretsen, Solveig; Engø-Monsen, Kenth; Aleem, Mohammad Abdul; Gurley, Emily Suzanne; Frigessi Di Rattalma, Arnoldo & de Blasio, Birgitte Freiesleben (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. ISSN 2399-3642. 3. doi: 10.1038/s42003-020-0884-6. Fulltekst i vitenarkiv
• Bergholtz, Helga; Lien, Tonje Gulbrandsen; Swanson, David; Frigessi Di Rattalma, Arnoldo; 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. ISSN 2374-4677. 6. doi: 10.1038/s41523-020-0167-x. Fulltekst i vitenarkiv
• Page, Christian; Djordjilovic, Vera; Nøst, Therese Haugdahl; Ghiasvand, Reza; Sandanger, Torkjel M & Frigessi Di Rattalma, Arnoldo [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. ISSN 2045-2322. 10, s. 1–8. doi: 10.1038/s41598-020-61430-3. Fulltekst i vitenarkiv
• 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. ISSN 2041-1723. 10:5499, s. 1–14. doi: 10.1038/s41467-019-13329-5. Fulltekst i vitenarkiv
• Swanson, David; Lien, Tonje Gulbrandsen; Bergholtz, Helga; Sørlie, Therese & Frigessi Di Rattalma, Arnoldo (2019). A Bayesian two-way latent structure model for genomic data integration reveals few pan-genomic cluster subtypes in a breast cancer cohort. Bioinformatics. ISSN 1367-4803. 35(23), s. 4886–4897. doi: 10.1093/bioinformatics/btz381. Vis sammendrag

  Motivation Unsupervised clustering is important in disease subtyping, among having other genomic applications. As genomic data has become more multifaceted, how to cluster across data sources for more precise subtyping is an ever more important area of research. Many of the methods proposed so far, including iCluster and Cluster of Cluster Assignments (COCAs), make an unreasonable assumption of a common clustering across all data sources, and those that do not are fewer and tend to be computationally intensive. Results We propose a Bayesian parametric model for integrative, unsupervised clustering across data sources. In our two-way latent structure model, samples are clustered in relation to each specific data source, distinguishing it from methods like COCAs and iCluster, but cluster labels have across-dataset meaning, allowing cluster information to be shared between data sources. A common scaling across data sources is not required, and inference is obtained by a Gibbs Sampler, which we improve with a warm start strategy and modified density functions to robustify and speed convergence. Posterior interpretation allows for inference on common clusterings occurring among subsets of data sources. An interesting statistical formulation of the model results in sampling from closed-form posteriors despite incorporation of a complex latent structure. We fit the model with Gaussian and more general densities, which influences the degree of across-dataset cluster label sharing. Uniquely among integrative clustering models, our formulation makes no nestedness assumptions of samples across data sources so that a sample missing data from one genomic source can be clustered according to its existing data sources. We apply our model to a Norwegian breast cancer cohort of ductal carcinoma in situ and invasive tumors, comprised of somatic copy-number alteration, methylation and expression datasets. We find enrichment in the Her2 subtype and ductal carcinoma among those observations exhibiting greater cluster correspondence across expression and CNA data. In general, there are few pan-genomic clusterings, suggesting that models assuming a common clustering across genomic data sources might yield misleading results. Availability and implementation The model is implemented in an R package called twl (‘two-way latent’), available on CRAN. Data for analysis are available within the R package.

• Liu, Qinghua; Reiner, Andrew Henry; Frigessi Di Rattalma, Arnoldo & Scheel, Ida (2019). Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows model. Knowledge-Based Systems. ISSN 0950-7051. s. 1–12. doi: 10.1016/j.knosys.2019.104960. Fulltekst i vitenarkiv
• 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
• Crispino, Marta; Arjas, Elja; Vitelli, Valeria; Barrett, Natasha & Frigessi Di Rattalma, Arnoldo (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
• Engebretsen, Solveig; Engø-Monsen, Kenth; Frigessi Di Rattalma, Arnoldo & De Blasio, Birgitte Freiesleben (2019). A theoretical single-parameter model for urbanisation to study infectious disease spread and interventions. PLoS Computational Biology. ISSN 1553-734X. 15(3). doi: 10.1371/journal.pcbi.1006879. Fulltekst i vitenarkiv
• Liu, Qinghua; Crispino, Marta; Scheel, Ida; Vitelli, Valeria & Frigessi Di Rattalma, Arnoldo (2019). Model-based learning from preference data. Annual Review of Statistics and Its Application. ISSN 2326-8298. 6, s. 329–354. doi: 10.1146/annurev-statistics-031017-100213.
• Sørensen, Øystein; Hellton, Kristoffer Herland; Frigessi Di Rattalma, Arnoldo & Thoresen, Magne (2018). Covariate Selection in High-Dimensional Generalized Linear Models With Measurement Error. Journal of Computational And Graphical Statistics (JCGS). ISSN 1061-8600. 27(4), s. 739–749. doi: 10.1080/10618600.2018.1425626. Fulltekst i vitenarkiv Vis sammendrag

  In many problems involving generalized linear models, the covariates are subject to measurement error. When the number of covariates p exceeds the sample size n, regularized methods like the lasso or Dantzig selector are required. Several recent papers have studied methods which correct for measurement error in the lasso or Dantzig selector for linear models in the p > n setting. We study a correction for generalized linear models, based on Rosenbaum and Tsybakov’s matrix uncertainty selector. By not requiring an estimate of the measurement error covariance matrix, this generalized matrix uncertainty selector has a great practical advantage in problems involving high-dimensional data. We further derive an alternative method based on the lasso, and develop efficient algorithms for both methods. In our simulation studies of logistic and Poisson regression with measurement error, the proposed methods outperform the standard lasso and Dantzig selector with respect to covariate selection, by reducing the number of false positives considerably. We also consider classification of patients on the basis of gene expression data with noisy measurements. Supplementary materials for this article are available online.

• Rohrbeck, Christian; Costain, Deborah A & Frigessi Di Rattalma, Arnoldo (2018). Bayesian spatial monotonic multiple regression. Biometrika. ISSN 0006-3444. 105(3), s. 691–707. doi: 10.1093/biomet/asy019.
• LeBlanc, Marissa; Zuber, Verena; Thompson, Wesley Kurt; Andreassen, Ole Andreas; Frigessi Di Rattalma, Arnoldo & Kulle, Bettina (2018). A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework. BMC Genomics. ISSN 1471-2164. 19:494, s. 1–15. doi: 10.1186/s12864-018-4859-7. Fulltekst i vitenarkiv
• Erango, Markos Abiso; Frigessi Di Rattalma, Arnoldo & Rosseland, Leiv Arne (2018). A three minutes supine position test reveals higher risk of spinal anesthesia induced hypotension during cesarean delivery. An observational study. [version 1; peer review: 3 approved]. F1000 Research. ISSN 2046-1402. 7(1028), s. 1–20. doi: 10.12688/F1000RESEARCH.15142.1. Fulltekst i vitenarkiv
• Rohrbeck, Christian; Eastoe, Emma F.; Frigessi Di Rattalma, Arnoldo & Tawn, Jonathan A. (2018). Extreme value modelling of water-related insurance claims. Annals of Applied Statistics. ISSN 1932-6157. 12(1), s. 246–282. doi: 10.1214/17-AOAS1081. Fulltekst i vitenarkiv
• Vitelli, Valeria; Sørensen, Øystein; Crispino, Marta; Frigessi Di Rattalma, Arnoldo & Arjas, Elja (2018). Probabilistic preference learning with the Mallows rank model. Journal of machine learning research. ISSN 1532-4435. 18, s. 1–49. Fulltekst i vitenarkiv
• Engebretsen, Solveig; Frigessi, Arnoldo; Engø-Monsen, Kenth; Furberg, Anne-Sofie; Stubhaug, Audun & De Blasio, Birgitte Freiesleben [Vis alle 7 forfattere av denne artikkelen] (2018). The peer effect on pain tolerance. Scandinavian Journal of Pain. ISSN 1877-8860. 18(3), s. 467–477. doi: 10.1515/sjpain-2018-0060. Fulltekst i vitenarkiv
• Asfaw, Derbachew; Vitelli, Valeria; Sørensen, Øystein; Arjas, Elja & Frigessi, Arnoldo (2017). Time-varying rankings with the Bayesian Mallows model. Stat. ISSN 2049-1573. 6(1), s. 14–30. doi: 10.1002/sta4.132. Fulltekst i vitenarkiv
• Fleischer, Thomas; Tekpli, Xavier; Mathelier, Anthony; Wang, Shixiong; Nebdal, Daniel J.H. & Dhakal, Hari Prasad [Vis alle 16 forfattere av denne artikkelen] (2017). DNA methylation at enhancers identifies distinct breast cancer lineages. Nature Communications. ISSN 2041-1723. 8(1379). doi: 10.1038/s41467-017-00510-x. Fulltekst i vitenarkiv
• Linder, Dennis; Frigessi, Arnoldo; Piaserico, Stefano & Keilman, Nico (2017). Simulating the life course of psoriasis patients: the interplay between therapy intervention and marital status. Journal of the European Academy of Dermatology and Venereology. ISSN 0926-9959. 32(1), s. 62–67. doi: 10.1111/jdv.14567. Fulltekst i vitenarkiv Vis sammendrag

  Background Psoriasis, a chronic relapsing inflammatory disease affecting primarily the skin, shows multiple comorbidities including depression, cardiovascular diseases and other relevant conditions. Psoriasis patients experience social isolation, job loss, financial difficulties and partnership problems. Inversely, psychosocial impairments may negatively influence the disease course. Objective To explore the feasibility of a model describing the interaction of psychosocial and clinical factors over the life course of patients. Methods We considered only seven states for members of a hypothetical population: single and healthy, single and having a psoriasis flare, single and ‘cured’, coupled and healthy, coupled and having a psoriasis flare, coupled and ‘cured’, and dead. Transition probabilities between states were taken from the Norwegian Population Register for the healthy population and from epidemiological research articles. Clinical experience allowed adjustments on the assumed parameters. Results Our macromodel, which simulates the effect of therapy intervention on patients’ partnership status, yields a description of the transitions between the seven states. Treatment efficacy shows only a negligible effect on the chances of living with a partner. Conclusions Mathematical modelling of interactions between social and health variables is in principle feasible. However, complex models, comprising more variables (for instance: employment status, depression level, obesity etc.), are needed for more realistic simulations for the interactions studied. As increasing the number of variables leads to an exponential increase of the model’s state space, switching to micromodelling (representing each individual separately) may be necessary.

• Belay, Denekew Bitew; Kifle, Yehenew Getachew; Goshu, Ayele Taye; Gran, Jon Michael; Yewhalaw, Delenasaw & Duchateau, Luc [Vis alle 7 forfattere av denne artikkelen] (2017). Joint Bayesian modeling of time to malaria and mosquito abundance in Ethiopia. BMC Infectious Diseases. ISSN 1471-2334. 17(1). doi: 10.1186/s12879-017-2496-4. Fulltekst i vitenarkiv
• Aure, Miriam Ragle; Vitelli, Valeria; Jernström, Sandra Johanna; Kumar, Surendra; Krohn, Marit & Due, Eldri Undlien [Vis alle 34 forfattere av denne artikkelen] (2017). Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome. Breast Cancer Research. ISSN 1465-5411. 19. doi: 10.1186/s13058-017-0812-y. Fulltekst i vitenarkiv Vis sammendrag

  Background Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Methods Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a “cluster-of-clusters” approach with consensus clustering. Results Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed. Conclusions The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.

• Frigessi, Arnoldo; Buhlmann, Peter; Glad, Ingrid Kristine; Richardson, Sylvia & Vannucci, Marina (2016). Some themes in high-dimensional statistics. Abel Symposia. ISSN 2193-2808. 11, s. 1–13. doi: 10.1007/978-3-319-27099-9_1.
• Crispino, Marta; Arjas, Elja; Vitelli, Valeria & Frigessi, Arnoldo (2016). Recommendation from intransitive pairwise comparisons. CEUR Workshop Proceedings. ISSN 1613-0073. 1688.
• Solbergersen, LInn Cecilie; Ahmed, Ismail; Frigessi, Arnoldo; Glad, Ingrid Kristine & Richardson, Sylvia Therese Lamblin (2016). Preselection in Lasso-Type Analysis for Ultra-High Dimensional Genomic Exploration. Abel Symposia. ISSN 2193-2808. 11, s. 37–66. doi: 10.1007/978-3-319-27099-9_3.
• Tharmaratnam, K.; Sperrin, M.; Jaki, T.; Reppe, Sjur & Frigessi, Arnoldo (2016). Tilting the lasso by knowledge-based post-processing. BMC Bioinformatics. ISSN 1471-2105. 17(344), s. 1–9. doi: 10.1186/s12859-016-1210-7.

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• Aalen, Odd O.; Frigessi, Arnoldo; Moger, Tron Anders; Scheel, Ida; Skovlund, Eva & Veierød, Marit Bragelien (2018). Statistiske metoder i medisin og helsefag. 2. utgave. Gyldendal Akademisk. ISBN 978-82-05-50764-7. 351 s.
• Frigessi, Arnoldo; Buhlmann, A; Glad, I. K.; Langaas, M; Richardson, S & Vannicci, M (2016). Statistical analysis for high-dimensional data. The Abel Symposium 2014. Springer. ISBN 978-3-319-27097-5. 360 s.
• Frigessi, Arnoldo; Bühlmann, Peter; Glad, Ingrid Kristine; Langaas, Mette; Richardson, Sylvia Therese Lamblin & Vannucci, Marina (2016). Statistical Analysis for High-Dimensional Data. Springer. ISBN 978-3-319-27097-5. 306 s.

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• Frigessi, Arnoldo (2024). Kunstig intelligens for anestesiforskning og praksis .
• Frigessi, Arnoldo (2024). Supporting the AI revolution.
• Frigessi, Arnoldo (2023). From limited patient data, to high frequency synthetic data, to the differential equation of a breast tumour growth.
• Frigessi, Arnoldo (2023). The AI algorithm that you are using has its own agenda: you might want to know it. .
• Frigessi, Arnoldo (2023). AI i medisin og klinikk.
• Frigessi, Arnoldo (2023). Kunstig intelligens, algoritmer i klinisk praksis, hvor er vi, hva skjer nå, muligheter og utfordringer .
• Frigessi, Arnoldo (2023). Pair copula constructions in machine learning.
• Frigessi, Arnoldo (2023). The AI ecosystem.
• Frigessi, Arnoldo (2023). Hjernen i sentrum: Kunstig intelligens Hva er best; kunstig eller ekte intelligens? .
• Frigessi, Arnoldo; Smajdor, Anna & Zucknick, Manuela (2023). The Importance of Reward Functions in Machine Learning Algorithms and Why These Should be Regulated. 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

• 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. ISSN 2234-943X. 13, s. 1–16. doi: 10.3389/fonc.2023.1048593. 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.
• Frigessi, Arnoldo (2022). A Bayesian two-way latent structure clustering model for multiple data integration, with an application to subtyping breast cancer.
• Frigessi, Arnoldo (2022). Probabilistic preference learning from incomplete rank data .
• Andreassen, Tor Arne; Frigessi, Arnoldo & Spurkland, Anne (2022). Omikron vil neppe smitte alle. Men en eller annen variant, vil nesten alle bli smittet av. [Avis]. Aftenposten.
• Frigessi, Arnoldo (2021). The Norwegian regional stochastic model of the Covid-19 epidemics.
• Frigessi, Arnoldo (2021). Math&Stats modelling of breast cancer therapy .
• Frigessi, Arnoldo (2021). Covid-19 modelling and AI.
• Frigessi, Arnoldo (2021). Data science in emergency.
• Frigessi, Arnoldo (2021). Models and predictions during the Covid-19 emergency in Norway.
• Frigessi, Arnoldo (2021). Sverdrup prisen 2021.
• Frigessi, Arnoldo (2021). Data science in an emergency.
• Frigessi, Arnoldo (2021). Mathematical modelling of the Sars-CoV-2 pandemic in Norway.
• Frigessi, Arnoldo (2021). Data Science for the covid-19 emergency.
• Frigessi, Arnoldo (2021). Debatt i NRK radio P2 om utnyttelse av norske helsedata. [Radio]. NRK P2.
• Stoltenberg, Camilla; Frigessi Di Rattalma, Arnoldo; Engebretsen, Solveig; Sandberg, Hallvard; Johansen, Per Anders & Ruhlin Gjuvsland, Elin (2021). Paneldebatt: HVA KAN VI LÆRE AV RUNDEN MED COVID: HVORDAN HÅNDTERER VI USIKKERHET OG ÅPENHET? [Internett]. Arendalsuka.
• Stoltenberg, Camilla; Aavitsland, Preben; Rø, Gunnar Øyvind Isaksson; de Blasio, Birgitte Freiesleben; Frigessi Di Rattalma, Arnoldo & Engebretsen, Solveig (2021). Aftenposten bommer om prognoser. Igjen og igjen. . Aftenposten (morgenutg. : trykt utg.). ISSN 0804-3116.
• Frigessi Di Rattalma, Arnoldo; 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.
• Frigessi Di Rattalma, Arnoldo (2020). The Norwegian Covid-19 Model: a spatial modelling of the early phase epidemics .
• Frigessi Di Rattalma, Arnoldo (2020). Spatial modelling of early-phase COVID-19 epidemic in Norway.
• Frigessi Di Rattalma, Arnoldo (2020). In silico modelling of breast tumours for personalised therapy.
• Løland, Anders & Frigessi Di Rattalma, Arnoldo (2020). Episode 3: Hvorfor skal forskning være en del av beredskapen vår? Med Arnoldo Frigessi. [Internett]. Sannsynligvis VIKTIG (podkast).
• Løland, Anders & Frigessi Di Rattalma, Arnoldo (2020). Episode 2: Om den essensielle usikkerheten. Med Arnoldo Frigessi. [Internett]. Sannsynligvis VIKTIG (podkast).
• Lai, Xiaoran; Kohn Luque, Alvaro; Frigessi, Arnoldo & Geier, Oliver (2017). Breast MRI as a building block for patient-specific simulations of drug effect.
• Frigessi, Arnoldo; Buhlmann, Peter; Glad, Ingrid Kristine; Langaas, Mette; Richardson, Sylvia Therese Lamblin & Vannucci, Marina (2016). Abel Symposia: Preface. Abel Symposia. ISSN 2193-2808. 11, s. vii–x.
• Frigessi, Arnoldo (2016). Discovery and prediction of change .
• Frigessi, Arnoldo (2016). Big Data Big Insight.
• Frigessi, Arnoldo (2016). Big Health data.
• Frigessi, Arnoldo (2016). The power of statistical science: from data to discovery and value .
• Frigessi, Arnoldo; Vitelli, Valeria; Crispino, Marta & Arjas, Elja (2016). Recommendations from non-transitive pairwise comparisons.
• Frigessi, Arnoldo (2016). A high-dimensional tribute to Juni Palmgren.
• Frigessi, Arnoldo (2016). Value from sharing data .
• Frigessi, Arnoldo (2016). From big data to big insight .
• Frigessi, Arnoldo (2016). Bayesian Inference for Ranks .
• Frigessi, Arnoldo (2016). My data science.
• Frigessi, Arnoldo (2016). Big health data: fantastiske muligheter og utfordringer .
• Frigessi, Arnoldo (2016). The value of uncertainty .
• 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.
• 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.
• Vitelli, Valeria; Sørensen, Øystein; Frigessi, Arnoldo & Arjas, Elja (2018). Probabilistic preference learning with the Mallows rank model. Journal of Machine Learning Research.

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