Arnoldo Frigessi

• Bergholtz, Helga; Lien, Tonje Gulbrandsen; Swanson, David; Frigessi Di Rattalma, Arnoldo; Daidone, Maria Grazia; Tost, Jörg; Wärnberg, Fredrik & Sørlie, Therese (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.
• 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

• Page, Christian; Djordjilovic, Vera; Nøst, Therese Haugdahl; Ghiasvand, Reza; Sandanger, Torkjel M; Frigessi Di Rattalma, Arnoldo; Thoresen, Magne & Veierød, Marit Bragelien (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.
• Pladsen, Arne Valebjørg; Nilsen, Gro; Rueda, Oscar M.; Aure, Miriam Ragle; Borgan, Ørnulf; Liestøl, Knut; Vitelli, Valeria; Frigessi Di Rattalma, Arnoldo; Langerød, Anita; Mathelier, Anthony; Engebråten, Olav; Kristensen, Vessela N.; Wedge, David C.; Van Loo, Peter; Caldas, Carlos; Børresen-Dale, Anne-Lise; Russnes, Hege Elisabeth Giercksky & Lingjærde, Ole Christian (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.
• 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.
• Lai, Xiaoran; Geier, Oliver; Fleischer, Thomas; Garred, Øystein; Borgen, Elin; Funke, Simon Wolfgang; Kumar, Surendra; Rognes, Marie Elisabeth; Seierstad, Therese; Børresen-Dale, Anne-Lise; Kristensen, Vessela N.; Engebråten, Olav; Kohn Luque, Alvaro & Frigessi Di Rattalma, Arnoldo (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.
• 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
• 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.
• 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.

• Tekpli, Xavier; Lien, Tonje Gulbrandsen; Røssevold, Andreas Hagen; Nebdal, Daniel J.H.; Borgen, Elin; Ohnstad, Hege Oma; Kyte, Jon A; Vallon-Christersson, Johan; Fongaard, Marie; Due, Eldri Undlien; Svartdal, Lisa Gregusson; Sveli, My Anh Tu; Garred, Øystein; Frigessi Di Rattalma, Arnoldo; Sahlberg, Kristine Kleivi; Sørlie, Therese; Russnes, Hege Elisabeth Giercksky; Naume, Bjørn & Kristensen, Vessela N. (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.
• Engebretsen, Solveig; Frigessi, Arnoldo; Engø-Monsen, Kenth; Furberg, Anne-Sofie; Stubhaug, Audun; De Blasio, Birgitte Freiesleben & Nielsen, Christopher Sivert (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.
• 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.. F1000 Research.  ISSN 2046-1402.  7(1028) . doi: 10.12688/f1000research.15142.1 Fulltekst i vitenarkiv.
• 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.
• 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
• 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.
• 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.

• 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.
• 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.
• Aure, Miriam Ragle; Vitelli, Valeria; Jernström, Sandra Johanna; Kumar, Surendra; Krohn, Marit; Due, Eldri Undlien; Haukaas, Tonje Husby; Leivonen, Suvi-Katri; Vollan, Hans Kristian Moen; Luders, Torben; Rødland, Einar Andreas; Vaske, Charles; Zhao, Wei; Møller, Elen Kristine; Nord, Silje; Giskeødegård, Guro F.; Bathen, Tone Frost; Caldas, Carlos; Tramm, Trine; Alsner, Jan; Overgaard, Jens; Geisler, Jürgen; Bukholm, Ida Rashida Khan; Naume, Bjørn; Schlichting, Ellen; Sauer, Torill; Mills, Gordon B.; Kåresen, Rolf; Mælandsmo, Gunhild; Lingjærde, Ole Christian; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Sahlberg, Kristine Kleivi & OSBREAC, Oslo Breast Cancer Consortium (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.

• Belay, Denekew Bitew; Kifle, Yehenew Getachew; Goshu, Ayele Taye; Gran, Jon Michael; Yewhalaw, Delenasaw; Duchateau, Luc & Frigessi, Arnoldo (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.
• Fleischer, Thomas; Tekpli, Xavier; Mathelier, Anthony; Wang, Shixiong; Nebdal, Daniel J.H.; Dhakal, Hari Prasad; Sahlberg, Kristine Kleivi; Schlichting, Ellen; Børresen-Dale, Anne-Lise; Borgen, Elin; Naume, Bjørn; Eskeland, Ragnhild; Frigessi, Arnoldo; Tost, Jörg; Rodriguez, Antoni Hurtado & Kristensen, Vessela N. (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.

• Crispino, Marta; Arjas, Elja; Vitelli, Valeria & Frigessi, Arnoldo (2016). Recommendation from intransitive pairwise comparisons. CEUR Workshop Proceedings.  ISSN 1613-0073.  1688
• Bolin, David; Frigessi, Arnoldo; Guttorp, Peter; Haug, Ola; Orskaug, Elisabeth; Scheel, Ida & Wallin, Jonas (2016). Calibrating regionally downscaled precipitation over Norway through quantile-based approaches. Advances in Statistical Climatology, Meteorology and Oceanography (ASCMO).  ISSN 2364-3579.  2, s 39- 47 . doi: 10.5194/ascmo-2-39-2016 Fulltekst i vitenarkiv. Vis sammendrag

  Dynamical downscaling of earth system models is intended to produce high-resolution climate information at regional to local scales. Current models, while adequate for describing temperature distributions at relatively small scales, struggle when it comes to describing precipitation distributions. In order to better match the distribution of observed precipitation over Norway, we consider approaches to statistical adjustment of the output from a regional climate model when forced with ERA-40 reanalysis boundary conditions. As a second step, we try to correct downscalings of historical climate model runs using these transformations built from downscaled ERA-40 data. Unless such calibrations are successful, it is difficult to argue that scenario-based downscaled climate projections are realistic and useful for decision makers. We study both full quantile calibrations and several different methods that correct individual quantiles separately using random field models. Results based on cross-validation show that while a full quantile calibration is not very effective in this case, one can correct individual quantiles satisfactorily if the spatial structure in the data are accounted for. Interestingly, different methods are favoured depending on whether ERA-40 data or historical climate model runs are adjusted.

• 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
• Hobæk Haff, Ingrid; Aas, Kjersti; Frigessi, Arnoldo & Lacal Graziani, Virginia (2016). Structure learning in Bayesian Networks using regular vines. Computational Statistics & Data Analysis.  ISSN 0167-9473.  101, s 186- 208 . doi: 10.1016/j.csda.2016.03.003 Vis sammendrag

  Learning the structure of a Bayesian Network from multidimensional data is an important task in many situations, as it allows understanding conditional (in)dependence relations which in turn can be used for prediction. Current methods mostly assume a multivariate normal or a discrete multinomial model. A new greedy learning algorithm for continuous non-Gaussian variables, where marginal distributions can be arbitrary, as well as the dependency structure, is proposed. It exploits the regular vine approximation of the model, which is a tree-based hierarchical construction with pair-copulae as building blocks. It is shown that the networks obtainable with our algorithm belong to a certain subclass of chordal graphs. Chordal graphs representations are often preferred, as they allow very efficient message passing and information propagation in intervention studies. It is illustrated through several examples and real data applications that the possibility of using non-Gaussian margins and a nonnon-linear dependency structure outweighs the restriction to chordal graphs.

• 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
• Solvang, Hiroko Kato; Frigessi, Arnoldo; Kaveh, Fatemeh; Riis, Margit; Luders, Torben; Bukholm, Ida Rashida Khan; Kristensen, Vessela N. & Andreassen, Bettina Kulle (2016). Gene expression analysis supports tumor threshold over 2.0 cm for T-category breast cancer. EURASIP Journal on Bioinformatics and Systems Biology.  ISSN 1687-4145.  2016(6) . doi: 10.1186/s13637-015-0034-5 Fulltekst i vitenarkiv.
• 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
• Fairbairn, Catharine E.; Sayette, Michael; Aalen, Odd O. & Frigessi, Arnoldo (2015). Alcohol and Emotional Contagion An Examination of the Spreading of Smiles in Male and Female Drinking Groups. Clinical Psychological Science.  ISSN 2167-7026.  3(5), s 686- 701 . doi: 10.1177/2167702614548892
• Frigessi, Arnoldo; Holden, Lars & Teigland, André (2015). (sfi)<sup>2</sup> statistics for innovation - The experience of the Oslo centre in industrial statistics. Applied Stochastic Models in Business and Industry.  ISSN 1524-1904.  31(1), s 82- 93 . doi: 10.1002/asmb.2113
• Hobæk Haff, Ingrid; Frigessi, Arnoldo & Maraun, Douglas (2015). How well do regional climate models simulate the spatial dependence of precipitation? An application of pair-copula constructions. Journal of Geophysical Research (JGR): Atmospheres.  ISSN 2169-897X.  120(7), s 2624- 2646 . doi: 10.1002/2014JD022748 Vis sammendrag

  We investigate how well a suite of regional climate models (RCMs) from the ENSEMBLES project represents the residual spatial dependence of daily precipitation. The study area we consider is a 200 km × 200 km region in south central Norway, with RCMs driven by ERA-40 boundary conditions at a horizontal resolution of approximately 25 km × 25 km. We model the residual spatial dependence with pair-copula constructions, which allows us to assess both the overall and tail dependence in precipitation, including uncertainty estimates. The selected RCMs reproduce the overall dependence rather well, though the discrepancies compared to observations are substantial. All models overestimate the overall dependence in the west-east direction. They also overestimate the upper tail dependence in the north-south direction during winter, and in the west-east direction during summer, whereas they tend to underestimate this dependence in the north-south direction in summer. Moreover, many of the climate models do not simulate the small-scale dependence patterns caused by the pronounced orography well. However, the misrepresented residual spatial dependence does not seem to affect estimates of high quantiles of extreme precipitation aggregated over a few grid boxes. The underestimation of the area-aggregated extreme precipitation is due mainly to the well-known underestimation of the univariate margins for individual grid boxes, suggesting that the correction of RCM biases in precipitation might be feasible.

• LeBlanc, Marissa; Zuber, Verena; Kulle, Bettina; Witoelar, Aree; Zeng, Limin; Bettella, Francesco; Wang, Yunpeng; McEvoy, LK; Thompson, Kurt Wesley; Schork, AJ; Reppe, Sjur; Barrett-Connor, Elizabeth; Lighart, Symen; Dehghan, A; Gautvik, Kaare M; Nelson, CP; Schunkert, Heribert; Samani, Nilesh J; Ridker, PM; Chasman, Daniel I; Aukrust, Pål; Djurovic, Srdjan; Frigessi, Arnoldo; Desikan, RS; Dale, Anders & Andreassen, Ole Andreas (2015). Identifying Novel Gene Variants in Coronary Artery Disease and Shared Genes with Several Cardiovascular Risk Factors.. Circulation Research.  ISSN 0009-7330.  1, s 83- 94 . doi: 10.1161/CIRCRESAHA.115.306629
• Sørensen, Øystein; Frigessi, Arnoldo & Thoresen, Magne (2015). Measurement error in Lasso: Impact and likelihood bias correction. Statistica sinica.  ISSN 1017-0405.  25(2), s 809- 829 . doi: 10.5705/ss.2013.180 Fulltekst i vitenarkiv.
• Fleischer, Thomas; Frigessi, Arnoldo; Johnson, Kevin C.; Edvardsen, Hege; Touleimat, Nizar; Klajic, Jovana; Riis, Margit; Haakensen, Vilde Drageset; Wärnberg, Fredrik; Naume, Bjørn; Helland, Åslaug; Børresen-Dale, Anne-Lise; Tost, Jörg; Christensen, Brock C. & Kristensen, Vessela N. (2014). Genome-wide DNA methylation profiles in progression to in situ and invasive carcinoma of the breast with impact on gene transcription and prognosis. Genome Biology.  ISSN 1465-6906.  15, s 435 . doi: 10.1186/s13059-014-0435-x Fulltekst i vitenarkiv. Vis sammendrag

  Background Ductal carcinoma in situ (DCIS) of the breast is a precursor of invasive breast carcinoma. DNA methylation alterations are thought to be an early event in progression of cancer, and may prove valuable as a tool in clinical decision making and for understanding neoplastic development. Results We generate genome-wide DNA methylation profiles of 285 breast tissue samples representing progression of cancer, and validate methylation changes between normal and DCIS in an independent dataset of 15 normal and 40 DCIS samples. We also validate a prognostic signature on 583 breast cancer samples from The Cancer Genome Atlas. Our analysis reveals that DNA methylation profiles of DCIS are radically altered compared to normal breast tissue, involving more than 5,000 genes. Changes between DCIS and invasive breast carcinoma involve around 1,000 genes. In tumors, DNA methylation is associated with gene expression of almost 3,000 genes, including both negative and positive correlations. A prognostic signature based on methylation level of 18 CpGs is associated with survival of breast cancer patients with invasive tumors, as well as with survival of patients with DCIS and mixed lesions of DCIS and invasive breast carcinoma. Conclusions This work demonstrates that changes in the epigenome occur early in the neoplastic progression, provides evidence for the possible utilization of DNA methylation-based markers of progression in the clinic, and highlights the importance of epigenetic changes in carcinogenesis.

• Kristensen, Vessela N.; Lingjærde, Ole Christian; Russnes, Hege Elisabeth Giercksky; Vollan, Hans Kristian Moen; Frigessi, Arnoldo & Børresen-Dale, Anne-Lise (2014). Principles and methods of integrative genomic analyses in cancer. Nature Reviews Cancer.  ISSN 1474-175X.  14(5), s 299- 313 . doi: 10.1038/nrc3721
• Tramm, Trine; Mohammed, Hayat; Myhre, Simen; Kyndi, Marianne; Alsner, Jan; Børresen-Dale, Anne-Lise; Sørlie, Therese; Frigessi, Arnoldo & Overgaard, Jens (2014). Development and Validation of a Gene Profile Predicting Benefit of Postmastectomy Radiotherapy in Patients with High-Risk Breast Cancer: A Study of Gene Expression in the DBCG82bc Cohort. Clinical Cancer Research.  ISSN 1078-0432.  20(20), s 5272- 5280 . doi: 10.1158/1078-0432.CCR-14-0458 Fulltekst i vitenarkiv. Vis sammendrag

  Purpose: To identify genes predicting benefit of radiotherapy in patients with high-risk breast cancer treated with systemic therapy and randomized to receive or not receive postmastectomy radiotherapy (PMRT). Experimental Design: The study was based on the Danish Breast Cancer Cooperative Group (DBCG82bc) cohort. Gene-expression analysis was performed in a training set of frozen tumor tissue from 191 patients. Genes were identified through the Lasso method with the endpoint being locoregional recurrence (LRR). A weighted gene-expression index (DBCG-RT profile) was calculated and transferred to quantitative real-time PCR (qRT-PCR) in corresponding formalin-fixed, paraffin-embedded (FFPE) samples, before validation in FFPE from 112 additional patients. Results: Seven genes were identified, and the derived DBCG-RT profile divided the 191 patients into “high LRR risk” and “low LRR risk” groups. PMRT significantly reduced risk of LRR in “high LRR risk” patients, whereas “low LRR risk” patients showed no additional reduction in LRR rate. Technical transfer of the DBCG-RT profile to FFPE/qRT-PCR was successful, and the predictive impact was successfully validated in another 112 patients. Conclusions: A DBCG-RT gene profile was identified and validated, identifying patients with very low risk of LRR and no benefit from PMRT. The profile may provide a method to individualize treatment with PMRT

• Sandve, Geir Kjetil; Gundersen, Sveinung; Johansen, Morten; Glad, Ingrid Kristine; Gunathasan, Krishanthi; Holden, Lars; Holden, Marit; Liestøl, Knut; Nygård, Ståle; Nygaard, Vegard; Paulsen, Jonas; Rydbeck, Halfdan; Trengereid, Kai; Clancy, Trevor; Drabløs, Finn; Ferkingstad, Egil; Kalaš, Matúš; Lien, Tonje Gulbrandsen; Rye, Morten Beck; Frigessi, Arnoldo & Hovig, Johannes Eivind (2013). The Genomic HyperBrowser: an analysis web server for genome-scale data. Nucleic Acids Research (NAR).  ISSN 0305-1048.  41(W1), s W133- W141 . doi: 10.1093/nar/gkt342 Fulltekst i vitenarkiv. Vis sammendrag

  The immense increase in availability of genomic scale datasets, such as those provided by the ENCODE and Roadmap Epigenomics projects, presents unprecedented opportunities for individual researchers to pose novel falsifiable biological questions. With this opportunity, however, researchers are faced with the challenge of how to best analyze and interpret their genome-scale datasets. A powerful way of representing genome-scale data is as feature-specific coordinates relative to reference genome assemblies, i.e. as genomic tracks. The Genomic HyperBrowser (http://hyperbrowser.uio.no) is an open-ended web server for the analysis of genomic track data. Through the provision of several highly customizable components for processing and statistical analysis of genomic tracks, the HyperBrowser opens for a range of genomic investigations, related to, e.g., gene regulation, disease association or epigenetic modifications of the genome.

• Aldrin, Magne; Raastad, Ragnhild; Tvete, Ingunn Fride; Berild, Dag; Frigessi, Arnoldo; Leegaard, Truls Michael; Monnet, Dominique L.; Walberg, Mette & Müller, Fredrik (2013). Antibiotic resistance in hospitals: a ward-specific random effect model in a low antibiotic consumption environment. Statistics in Medicine.  ISSN 0277-6715.  32(8), s 1407- 1418 . doi: 10.1002/sim.5636 Vis sammendrag

  Association between previous antibiotic use and emergence of antibiotic resistance has been reported for several microorganisms. The relationship has been extensively studied, and although the causes of antibiotic resistance are multi-factorial, clear evidence of antibiotic use as a major risk factor exists. Most studies are carried out in countries with high consumption of antibiotics and corresponding high levels of antibiotic resistance, and currently, little is known whether and at what level the associations are detectable in a low antibiotic consumption environment. We conduct an ecological, retrospective study aimed at determining the impact of antibiotic consumption on antibiotic-resistant Pseudomonas aeruginosa in three hospitals in Norway, a country with low levels of antibiotic use. We construct a sophisticated statistical model to capture such low signals. To reduce noise, we conduct our study at hospital ward level. We propose a random effect Poisson or binomial regression model, with a reparametrisation that allows us to reduce the number of parameters. Inference is likelihood based. Through scenario simulation, we study the potential effects of reduced or increased antibiotic use. Results clearly indicate that the effects of consumption on resistance are present under conditions with relatively low use of antibiotic agents. This strengthens the recommendation on prudent use of antibiotics, even when consumption is relatively low.

• Løland, Anders; Huseby, Ragnar Bang; Hjort, Nils Lid & Frigessi, Arnoldo (2013). Statistical Corrections of Invalid Correlation Matrices. Scandinavian Journal of Statistics.  ISSN 0303-6898.  40(4), s 807- 824 . doi: 10.1111/sjos.12035 Vis sammendrag

  Suppose estimates are available for correlations between pairs of variables but that the matrix of correlation estimates is not positive definite. In various applications, having a valid correlation matrix is important in connection with follow-up analyses that might, for example, involve sampling from a valid distribution. We present new methods for adjusting the initial estimates to form a proper, that is, nonnegative definite, correlation matrix. These are based on constructing certain pseudo-likelihood functions, formed by multiplying together exact or approximate likelihood contributions associated with the individual correlations. Such pseudo-likelihoods may then be maximized over the range of proper correlation matrices. They may also be utilized to form pseudo-posterior distributions for the unknown correlation matrix, by factoring in relevant prior information for the separate correlations. We illustrate our methods on two examples from a financial time series and genomic pathway analysis.

• Scheel, Ida; Ferkingstad, Egil; Frigessi, Arnoldo; Haug, Ola; Hinnerichsen, Mikkel & Meze-Hausken, Elisabeth (2013). A Bayesian hierarchical model with spatial variable selection: the effect of weather on insurance claims. The Journal of the Royal Statistical Society, Series C (Applied Statistics).  ISSN 0035-9254.  62, s 85- 100 . doi: 10.1111/j.1467-9876.2012.01039.x Vis sammendrag

  Climate change will affect the insurance industry. We develop a Bayesian hierarchical statistical approach to explain and predict insurance losses due to weather events at a local geographic scale. The number of weather-related insurance claims is modelled by combining generalized linear models with spatially smoothed variable selection. Using Gibbs sampling and reversible jump Markov chain Monte Carlo methods, this model is fitted on daily weather and insurance data from each of the 319 municipalities which constitute southern and central Norway for the period 1997–2006. Precise out-of-sample predictions validate the model. Our results show interesting regional patterns in the effect of different weather covariates. In addition to being useful for insurance pricing, our model can be used for short-term predictions based on weather forecasts and for long-term predictions based on downscaled climate models.

• Brown, Andrew; Jensen, Jimmy Kristian; Nikolova, Y.S.; Djurovic, Srdjan; Agartz, Ingrid; ALONSO, ANDRES SERVER; Ferrell, RE; Manuck, S.B.; Mattingsdal, Morten; Melle, Ingrid; Hariri, Ahmad; Frigessi, Arnoldo & Andreassen, Ole Andreas (2012). Genetic variants affecting the neural processing of human facial expressions: evidence using a genome-wide functional imaging approach. Translational Psychiatry.  ISSN 2158-3188. . doi: 10.1038/tp.2012.67
• di Serio, C; Pellin, Danilo; ambrosi, alessandro; Glad, Ingrid Kristine & Frigessi, Arnoldo (2012). Biostatistics Meets Bioinformatics in Integrating Information from Highdimensional Heterogeneous Genomic Data: Two Examples from Rare Genetic Diseases and Infectious Diseases, In E Biganzoli; A Vellido; F Ambrogi & R Tagliaferri (ed.),  Computational Intelligence methods for bioinformatics and biostatistics.  Springer.  ISBN 978-3-642-35686-5.  Lectures in Computer Science vol 7548.  s 10 - 20
• Joshi, Himanshu; Nordgard, Silje H.; Frigessi, Arnoldo; Børresen-Dale, Anne-Lise & Kristensen, Vessela N. (2012). Overrepresentation of transcription factor families in the genesets underlying breast cancer subtypes. BMC Genomics.  ISSN 1471-2164.  13(199) . doi: 10.1186/1471-2164-13-199
• LeBlanc, Marissa; Kulle, Bettina; Sundet, Kjetil Søren; Agartz, Ingrid; Melle, Ingrid; Djurovic, Srdjan; Frigessi, Arnoldo & Andreassen, Ole Andreas (2012). Genome-wide study identifies PTPRO and WDR72 and FOXQ1-SUMO1P1 interaction associated with neurocognitive function. Journal of Psychiatric Research.  ISSN 0022-3956.  46(2), s 271- 278 . doi: 10.1016/j.jpsychires.2011.11.001
• Villani, Antonella; Frigessi, Arnoldo; Liljeros, Fredrik; Nordvik, Monika K. & De Blasio, Birgitte Freiesleben (2012). A Characterization of Internet Dating Network Structures among Nordic Men Who Have Sex with Men. PLOS ONE.  ISSN 1932-6203.  7(7) . doi: 10.1371/journal.pone.0039717

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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 (ed.) (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 (ed.) (2016). Statistical Analysis for High-Dimensional Data. Springer.  ISBN 978-3-319-27097-5.  306 s.

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• 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.no.
• Frigessi Di Rattalma, Arnoldo (2020). In silico modelling of breast tumours for personalised therapy.
• Frigessi Di Rattalma, Arnoldo (2020). Spatial modelling of early-phase COVID-19 epidemic in Norway.
• Frigessi Di Rattalma, Arnoldo (2020). The Norwegian Covid-19 Model: a spatial modelling of the early phase epidemics.
• 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.
• Løland, Anders & Frigessi Di Rattalma, Arnoldo (2020, 03. juni). Episode 2: Om den essensielle usikkerheten. Med Arnoldo Frigessi. [Internett].  Sannsynligvis VIKTIG (podkast).
• Løland, Anders & Frigessi Di Rattalma, Arnoldo (2020, 09. juni). Episode 3: Hvorfor skal forskning være en del av beredskapen vår? 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; 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). Bayesian Inference for Ranks.
• Frigessi, Arnoldo (2016). Big Data Big Insight.
• Frigessi, Arnoldo (2016). Big Health data.
• Frigessi, Arnoldo (2016). Big health data: fantastiske muligheter og utfordringer.
• Frigessi, Arnoldo (2016). BigInsight and Skatteetatten.
• Frigessi, Arnoldo (2016). Discovery and prediction of change.
• Frigessi, Arnoldo (2016). From big data to big insight.
• Frigessi, Arnoldo (2016). My data science.
• Frigessi, Arnoldo (2016). The data rich society of the future.
• Frigessi, Arnoldo (2016). The power of statistical science: from data to discovery and value.
• Frigessi, Arnoldo (2016). The value of uncertainty.
• Frigessi, Arnoldo (2016). Value from sharing data.
• 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 & Løland, Anders (2016, 11. februar). Forskning på statistiske modeller kan gi store gevinster. [Fagblad].  Forskningsnytt 2015.
• Haug, Ola; Bolin, David; Frigessi, Arnoldo; Guttorp, Peter; Orskaug, Elisabeth; Scheel, Ida & Wallin, Jonas (2016). Modelling and predicting residential water damage insurance claims via a calibrated dynamical downscaling.
• Vitelli, Valeria; Sørensen, Øystein; Frigessi, Arnoldo & Arjas, Elja (2015). Probabilistic preference learning with the Mallows rank model.
• Frigessi, Arnoldo (2015). Apollon lansering.
• Frigessi, Arnoldo (2015). Bayesian Mallows model for preference learning.
• Frigessi, Arnoldo (2015, 16. november). De svakes ridder. [Tidsskrift].  Apollon.
• Frigessi, Arnoldo (2015, 14. august). Her er likheten mellom kreft og skattejuks. [Tidsskrift].  Apollon.
• Frigessi, Arnoldo (2015, 01. august). Innovasjon vs. personvern.  ComputerWorld.
• Frigessi, Arnoldo (2015). Ledermøte: presentasjon av BigInsight.
• Frigessi, Arnoldo (2015). Meta analysis with Mallows rank model.
• Frigessi, Arnoldo (2015). Pan Cancer models.
• Frigessi, Arnoldo (2015). Preference learning.
• Frigessi, Arnoldo (2015). SFI Big Insight - Norsk Regnesentral/UiO.
• Frigessi, Arnoldo (2015). Stabmøte "What is happening in biostatistics".
• Frigessi, Arnoldo (2015). The future of biostatistics and the Oslo Center for Biostatistics and Epidemiology.
• Frigessi, Arnoldo (2015). kurs STK1110, om big data og fremtiden i statistikk.
• Haug, Ola; Frigessi, Arnoldo; Scheel, Ida & Guttorp, Peter (2015). Modelling and predicting residential water damage insurance claims in a climate change perspective.
• Hobæk Haff, Ingrid; Frigessi, Arnoldo & Maraun, Douglas (2015). How well do regional climate models simulate the spatial dependence of precipitation? An application of pair-copula constructions.
• Hobæk Haff, Ingrid; Frigessi, Arnoldo & Maraun, Douglas (2015). Hvor godt klarer regionale klimamodeller å gjenskape den romlige avhengigheten i nedbør?.
• Frigessi, Arnoldo (2014). Bayesian inference for ranks. Lecture Notes in Computer Science (LNCS).  ISSN 0302-9743.  8819, s XIII- XIV . doi: 10.1007/978-3-319-12571-8
• Haug, Ola; Scheel, Ida; Orskaug, Elisabeth; Frigessi, Arnoldo; Guttorp, Peter & Ferkingstad, Egil (2014). Vulnerability models for water damage insurance claims - predictions of future losses in a climate change perspective.
• Haug, Ola; Orskaug, Elisabeth; Scheel, Ida; Frigessi, Arnoldo; Maraun, Douglas & Guttorp, Peter (2012). Evaluation and Calibration of Dynamically Downscaled Precipitation over Norwegian Mainland.
• Haug, Ola; Orskaug, Elisabeth; Scheel, Ida; Frigessi, Arnoldo; Maraun, Douglas & Guttorp, Peter (2012). Evaluation and calibration of dynamically downscaled precipitation over Norwegian mainland.
• Hobæk Haff, Ingrid; Frigessi, Arnoldo; Aas, Kjersti & Borgan, Ørnulf (2012). Pair-copula constructions - an inferential perspective. Series of dissertations submitted to the Faculty of Mathematics and Natural Sciences, University of Oslo.. 1218.
• Holden, Lars; Frigessi, Arnoldo & Mysterud, Rønnaug Sægrov (2012). Statistics for innovation, presentasjon av en SFI.
• Scheel, Ida; Haug, Ola; Orskaug, Elisabeth; Frigessi, Arnoldo & Guttorp, Peter (2012). Evaluating and Calibrating Dynamically Downscaled Precipitation Using the Doksum Shift Function.
• Tramm, T; Mohammed, H; Myhre, S; Alsner, J; Borresen-Dale, AL; Sorlie, T; Frigessi, Arnoldo & Overgaard, J (2012). A Seven-gene Signature Predicting Benefit of Postmastectomy Radiotherapy in High Risk Breast Cancer. European Journal of Cancer.  ISSN 0959-8049.  48, s S181- S181

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