Arnoldo Frigessi

• 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 Willemsen, Martijn C.; Larson, Martha & Corona, Humberto (Red.), RecSys '21: Fifteenth ACM Conference on Recommender Systems. Association for Computing Machinery (ACM). ISSN 978-1-4503-8458-2. 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.
• 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. s. 1–7. doi: 10.1080/23744235.2021.1977382. Fulltekst i vitenarkiv
• 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.
• 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
• 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. F1000 Research. ISSN 2046-1402. 7(1028). 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.
• Solvang, Hiroko Kato; Frigessi, Arnoldo; Kaveh, Fatemeh; Riis, Margit; Luders, Torben & Bukholm, Ida Rashida Khan [Vis alle 8 forfattere av denne artikkelen] (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
• Bolin, David; Frigessi, Arnoldo; Guttorp, Peter; Haug, Ola; Orskaug, Elisabeth & Scheel, Ida [Vis alle 7 forfattere av denne artikkelen] (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.

• 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.

• LeBlanc, Marissa; Zuber, Verena; Kulle, Bettina; Witoelar, Aree; Zeng, Limin & Bettella, Francesco [Vis alle 26 forfattere av denne artikkelen] (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.
• 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.

• 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.
• 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 [Vis alle 15 forfattere av denne artikkelen] (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. 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.

• Tramm, Trine; Mohammed, Hayat; Myhre, Simen; Kyndi, Marianne; Alsner, Jan & Børresen-Dale, Anne-Lise [Vis alle 9 forfattere av denne artikkelen] (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

• 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.

Se alle arbeider i Cristin

• 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.

Se alle arbeider i Cristin

• 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 .
• Haug, Ola; Bolin, David; Frigessi, Arnoldo; Guttorp, Peter; Orskaug, Elisabeth & Scheel, Ida [Vis alle 7 forfattere av denne artikkelen] (2016). Modelling and predicting residential water damage insurance claims via a calibrated dynamical downscaling.
• Frigessi, Arnoldo & Løland, Anders (2016). Forskning på statistiske modeller kan gi store gevinster. [Fagblad]. Forskningsnytt 2015.
• Frigessi, Arnoldo (2016). BigInsight and Skatteetatten.
• Frigessi, Arnoldo (2016). The data rich society of the future.
• Hobæk Haff, Ingrid; Frigessi, Arnoldo & Maraun, Douglas (2015). Hvor godt klarer regionale klimamodeller å gjenskape den romlige avhengigheten i nedbør?
• 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.
• Frigessi, Arnoldo (2015). SFI Big Insight - Norsk Regnesentral/UiO.
• 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.
• Frigessi, Arnoldo (2015). Stabmøte "What is happening in biostatistics".
• 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). Apollon lansering.
• Frigessi, Arnoldo (2015). Preference learning.
• Frigessi, Arnoldo (2015). Bayesian Mallows model for preference learning .
• Frigessi, Arnoldo (2015). Her er likheten mellom kreft og skattejuks. [Tidsskrift]. Apollon.
• Frigessi, Arnoldo (2015). Innovasjon vs. personvern. [Avis]. ComputerWorld.
• Frigessi, Arnoldo (2015). De svakes ridder. [Tidsskrift]. Apollon.
• Haug, Ola; Frigessi, Arnoldo; Scheel, Ida & Guttorp, Peter (2015). Modelling and predicting residential water damage insurance claims in a climate change perspective.
• 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.
• 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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