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Alvaro Köhn-Luque is an Associate Professor at the Oslo Center for Biostatistics and Epidemiology, University of Oslo, and Group Leader at the Department of Medical Genetics, Oslo University Hospital. His work bridges mathematical modeling with clinical applications, particularly in cancer research. His academic background includes a PhD in Mathematical and Computational Biology from Complutense University of Madrid (2012), preceded by multiple Master's degrees in Mathematics and Physics from Spanish universities. Dr. Köhn-Luque's research focuses on mathematical oncology , developing computational models to understand cancer dynamics and improve treatment strategies. His work spans multiscale modeling of tumor growth, personalized cancer medicine through computer simulations, and biomarker discovery using machine learning approaches. He has made significant contributions to modeling breast cancer progression and treatment response, particularly in the context of endocrine therapy and CDK4/6 inhibition. His recent publications demonstrate a strong trend toward integrating mechanistic learning approaches that combine mathematical models with machine learning techniques. This hybrid methodology allows for more accurate prediction of treatment outcomes while maintaining biological interpretability. His work frequently involves collaboration with clinical researchers to ensure models are grounded in real patient data and have direct translational potential. Computational modeling of tumor heterogeneity and drug response Development of methods for phenotypic deconvolution in cancer cell populations Integration of multi-omics data for personalized treatment prediction Application of birth-death processes to model tumor evolution Creation of user-friendly computational tools for biomedical researchers Dr. Köhn-Luque has supervised multiple PhD students including Even M Myklebust, Salim Ghannoum, and Xiaoran Lai, and has secured funding for projects including RESCUE, BigInsight, and Integreat. His research demonstrates a consistent trajectory from theoretical mathematical biology toward increasingly clinically relevant applications in personalized cancer medicine.

• Mathematical Oncology
• Computational Biology
• Biostatistics
• +7 more

Arnoldo Frigessi is Professor of Statistics at the University of Oslo, where he leads the Oslo Center for Biostatistics and Epidemiology and serves as director of BigInsight—a Centre of Excellence for Research-Based Innovation. This consortium unites industry, business, public actors, and academia to develop model-based machine learning methodologies for big data, with strong emphasis on health applications. His research centers on statistical methodology driven by real-world scientific challenges, specializing in stochastic models for complex dependence structures and computationally intensive inference algorithms. Core application domains include: Genomics and personalized cancer therapy (particularly breast and lung cancer) Infectious disease modeling (including pandemic response) eHealth, sensor data analysis, and recommender systems Personalized marketing and viral diffusion dynamics Analysis of his 15 most recent publications (2024-2025) reveals dominant themes in cancer systems biology , where he integrates multi-omics, single-cell transcriptomics, and computational modeling to decode tumor evolution under therapy. Parallel work advances infectious disease epidemiology through time-varying reproduction number estimation and mobility-based transmission modeling, while methodological innovations span synthetic data generation (TVineSynth), causal inference via target trial emulation, and Bayesian ranking models for recommender systems. Scientific Awards: No specific awards mentioned in source materials Frigessi actively supervises graduate students, including a Department of Informatics project on "Utilizing covariate information in recommender systems." His leadership of BigInsight—funded as a Research-Based Innovation Centre by the Research Council of Norway—secures major grants supporting interdisciplinary collaborations with industrial partners (e.g., Telenor, DNB) and public health institutions. Current projects integrate real-world clinical data with mechanistic models for treatment optimization. He directs BigInsight's multidisciplinary team of statisticians, computer scientists, and domain experts, while leading the Oslo Center for Biostatistics and Epidemiology's efforts in developing statistical frameworks for complex health data. These initiatives drive Norway's national strategy for data-driven health innovation.

• Statistics
• Biostatistics
• Machine Learning
• +5 more

Gunnar Morken is a Professor and senior consultant in the Department of Mental Health at the Norwegian University of Science and Technology (NTNU), affiliated with the Faculty of Medicine and Health Sciences and St. Olavs Hospital. His research focuses on bipolar disorder, chronotherapy, psychiatric epidemiology, and clinical interventions for mental health conditions such as schizophrenia and insomnia. He collaborates on studies involving genomic analysis, environmental factors (e.g., light exposure, UV radiation), and treatment efficacy evaluations. Key research areas include genomic insights into bipolar disorder, the role of circadian rhythms in mental health, and clinical trials assessing therapies like cognitive-behavioral therapy (CBT) for insomnia and exercise interventions for schizophrenia. His work spans both basic science (e.g., genetic studies) and translational research (e.g., hospital lighting design to improve sleep). Publications highlight his contributions to understanding lithium response mechanisms, environmental influences on psychiatric disorders, and the design of clinical trials in acute psychiatric care. Morken’s interdisciplinary approach integrates neuroscience, epidemiology, and clinical practice to advance mental health treatments.

• Bipolar Disorder
• Psychiatry
• Mental Health Treatment
• +5 more

Lill-Tove Rasmussen Busund is a Professor and Consultant Pathologist at the Department of Medical Biology, UiT The Arctic University of Norway. Her research focuses on translational cancer studies with emphasis on cancer biomarkers in tissue and blood, combining molecular biology with artificial intelligence applications in pathology. She leads the Translational Cancer Research Group (TCRG) and contributes to projects like the TNM-Immune Cell Score Trial and AI–Pathology initiatives. Role: Professor/Consultant Pathologist University: UiT The Arctic University of Norway Department: Department of Medical Biology Email: lill.tove.rasmussen.busund@uit.no Rasmussen Busund's work spans epidemiological cancer research , multi-omic data integration , and machine learning applications for cancer diagnostics. She develops prognostic and predictive biomarkers using population-based cohorts like the NOWAC study, with particular attention to hormone-dependent cancers and immune microenvironment analysis . Recent publications highlight her expertise in digital pathology and AI-driven tumor classification , including studies on microRNA expression patterns in breast and colon cancer, immune cell score development for NSCLC, and computational approaches to lymphocytic infiltration assessment. Her research directly impacts precision oncology and immunotherapy response prediction . She contributes to teaching as leader of the Musculoskeletal System course at the Medical School, and maintains active participation in scientific knowledge migration projects like the transition to the National Science Archive (NVA). Her ORCID identifier is 0000-0001-8235-6163 .

• Cancer Biomarkers
• Translational Cancer Research
• Digital Pathology
• +3 more

Victor Greiff is a Professor at the University of Oslo's Institute of Clinical Medicine , leading the Computational and Systems Immunology group. His research focuses on integrating high-throughput single-cell sequencing , artificial intelligence , and high-dimensional statistics to advance therapeutic antibody design, diagnostics, and vaccine development for infectious diseases, autoimmunity, and cancer. Key Research Areas : Computational immunology, adaptive immune receptor repertoires, machine learning in antibody discovery, bioinformatics, structural biology, and immune diagnostics. Funding : European Research Council (ERC) Grant AB-AG-INTERACT (Project ID: 101125630). Recent Publications highlight interdisciplinary work in: Machine learning for antibody design and immune repertoire classification . Structural analysis of antibody-antigen interactions and evolutionary immunogenetics . Development of bioinformatics tools (e.g., ExpoSeq, TCRpower, CompAIRR) for immune sequencing data analysis. Scientific Contributions include: Establishing computational frameworks for synthetic immune repertoire generation. Exploring genetic constraints on TCR and BCR diversity. Advancing AI-driven antibody engineering for improved therapeutic developability.

• Computational Immunology
• Adaptive Immune Receptor Repertoires
• Machine Learning in Antibody Design
• +5 more

Ben Michael Brumpton is an Associate Professor in the Department of Community Medicine and Nursing at the Norwegian University of Science and Technology (NTNU), affiliated with the Faculty of Medicine and Health Sciences. His research focuses on genetic epidemiology, particularly leveraging population-based health surveys (e.g., HUNT Study), electronic health records, and omics data to establish causal relationships in public health. Methodologically, he emphasizes Mendelian randomization and genetic epidemiology frameworks. Major Research Projects: HUNT Genotyping HUNT-COVID Questionnaire HUNT Metabolomics HUNT Methylation Research Interests: His work spans DNA methylation analysis, metabolomics, and the integration of omics data into epidemiological studies. He investigates causal links between genetic traits and diseases such as cardiovascular disorders, cancer, and respiratory conditions. Recent studies have explored intergenerational effects of maternal glycemic traits and parental BMI on offspring health outcomes. Publications Overview: His recent work highlights novel genetic loci for chronic low back pain, venous thromboembolism, and celiac disease. He has also contributed to large-scale meta-analyses of atrial fibrillation and breast cancer risk, emphasizing polygenic prediction and Mendelian randomization approaches. Labs/Teams: Primary affiliation with the HUNT Research Center and collaborations within NTNU’s epidemiology and genetics research groups.

• Genetic Epidemiology
• Mendelian Randomization
• Population-Based Health Surveys
• +5 more

Mette Marianne Svenning is a Professor at the Department of Arctic and Marine Biology at UiT The Arctic University of Norway. Her research focuses on microbial communities in Arctic and high-latitude ecosystems, particularly methanotrophs and their roles in methane cycling, permafrost dynamics, and climate change feedbacks. She investigates microbial adaptations to extreme environments, including cold-adapted methanotrophs and their metabolic pathways. Her work integrates genomic, transcriptomic, and biogeochemical approaches to understand microbial contributions to global carbon cycles. Key research themes include methane oxidation mechanisms, microbial community responses to environmental changes, and symbiotic relationships in Arctic seep ecosystems. She has conducted extensive fieldwork in Svalbard and other Arctic regions, contributing to long-term ecological studies at Ny-Ålesund. Her research also explores applications in biotechnology, such as microbial consortia for methane conversion to industrial products. Dr. Svenning collaborates internationally on projects addressing permafrost thaw impacts, microbial biogeography, and the interplay between vegetation, herbivory, and soil microbiota. Her findings highlight the critical role of Arctic microorganisms in modulating greenhouse gas emissions under climate change scenarios.

• Microbial Ecology
• Arctic Biogeochemistry
• Methane Cycling
• +3 more

Manuela Zucknick is Professor and Director of the Oslo Centre for Biostatistics and Epidemiology at the University of Oslo's Faculty of Medicine, Department of Biostatistics. Her research integrates statistical learning with translational cancer research to advance personalized medicine through multi-omics data integration. PhD Biostatistics, Imperial College London (2008) MSc Bioinformatics, Imperial College London (2004) Diplom Statistik, University of Dortmund (2003) Her research focuses on Bayesian methods for integrating heterogeneous data sources in cancer research, particularly for drug response prediction in pharmacogenomic screens and patient prognosis. She develops structured high-dimensional regression models for 'large p, small n' problems in molecular medicine, with emphasis on incorporating prior biological knowledge into risk prediction frameworks. Her work bridges statistical methodology with clinical applications in personalized cancer therapies. Her recent publications demonstrate consistent contributions to multi-omics integration and survival modeling across diverse clinical contexts including cancer, pregnancy complications, and rheumatoid arthritis. The research shows strong methodological innovation in handling high-dimensional biological data while maintaining clinical relevance. Through the Oslo Centre for Biostatistics and Epidemiology, she leads collaborative projects spanning oncology, obstetrics, and rheumatology. Her work frequently involves designing statistical frameworks for pharmacogenomic screens and developing tools for biomarker discovery in complex disease settings.

• Statistical learning for translational cancer research
• Personalized cancer therapies
• Multi-omics data integration
• +7 more

Zhi Zhao is a Researcher at the University of Oslo's Faculty of Medicine, affiliated with the department of Statistical learning in molecular medicine. His work focuses on statistical and computational methods applied to molecular medicine, including omics data analysis, pharmacogenomics, and cancer genomics. Research Interests Statistical learning for multi-omics data integration Bayesian variable selection in drug sensitivity studies Biomarker discovery for immune-related diseases Survival analysis modeling in clinical applications Key Contributions Developed R packages like BayesSUR for multivariate Bayesian regression and EnrichIntersect for set enrichment analysis. His recent work includes studies on immune checkpoint inhibitor toxicity biomarkers and pan-cancer drug response prediction. Awards No scientific awards explicitly mentioned in the provided text. Labs/Teams Core member of the Statistical learning in molecular medicine research group.

• Statistical Learning
• Molecular Medicine
• Bioinformatics
• +3 more

Maria Dung Cao is an Associate Professor at the Department of Nursing, Health and laboratory science at Oslo Metropolitan University (HioF). Her research focuses on medical biochemistry, molecular diagnostics, and cancer research, particularly breast cancer metabolism and biomarker discovery. She completed her PhD in Molecular Medicine (2012) at NTNU, Norway, with a thesis on MR metabolic characterization of breast cancer treatment effects. Her educational background includes a Master’s in Molecular Medicine (NTNU, 2008) and a Bachelor’s in Biomedical Science (HiØ, 2004). She has conducted research stays at Johns Hopkins University (USA) and Huddersfield University (UK). Key research interests include: Breast cancer metabolomics and metabolic profiling Choline phospholipid metabolism pathways Magnetic Resonance Spectroscopy (MRS) applications Epigenetic biomarkers in oncology Therapeutic target identification in cancer Recent work emphasizes metabolic response prediction to neoadjuvant chemotherapy and biomarker validation for treatment outcomes. Her publications highlight collaborations in international oncology research and translational metabolomics. No scientific awards are listed, but her extensive publication record demonstrates impactful contributions to cancer metabolomics and diagnostic biomarker research.

• Medical Biochemistry
• Biology
• Genetics
• +4 more

Ines Heiland is a Professor at the Department of Biomedicine, Faculty of Medicine, University of Bergen. Her research integrates systems biology and computational modeling to investigate metabolic networks, with a focus on NAD and tryptophan metabolism, mitochondrial function, and circadian regulation. University: University of Bergen School: Faculty of Medicine Department: Department of Biomedicine Position: Professor Email: i.heiland@uib.no Her educational background is not explicitly detailed in the provided texts, but her extensive research and supervisory roles indicate a strong academic foundation in systems biology and molecular metabolism. Ines Heiland's research centers on understanding how metabolic networks regulate cellular health and disease. She employs computational models to study NAD homeostasis, tryptophan catabolism, and their roles in aging, cancer, and neurodegenerative conditions. Her work emphasizes the integration of multi-omics data and kinetic modeling to uncover regulatory mechanisms in metabolic pathways. The recent articles highlight a consistent focus on NAD metabolism, metabolic modeling, and the interplay between metabolism and epigenetics. Themes include the evolution of metabolic pathways, subcellular compartmentation of metabolites, and the development of bioinformatics tools for metabolic analysis. Her publications span from algorithm development (e.g., GEMCAT) to physiological insights in aging and disease. She has contributed to editorial and review work, such as the 2020 editorial on systems biology challenges and the 2015 review on NAD metabolism dynamics, underscoring her leadership in the field. Supervised PhD student: Mathias Bockwoldt Ines Heiland has been involved in collaborative research projects, often in partnership with institutions like UiT The Arctic University of Norway. She participates in networks such as NORBIS and BioCat, contributing to interdisciplinary efforts in biotechnology and systems biology. Her work involves both theoretical modeling and experimental validation, particularly in the context of marine biomass conversion and Arctic bioplastics.

• Systems Biology
• Metabolic Modeling
• NAD Metabolism
• +7 more

Julia Debik is an Associate Professor at the Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), with a dual research role at the Musculoskeletal Research Group and CIMORe. She holds a PhD in Medical Technology and a Master of Science in Industrial Mathematics from NTNU, combining computational expertise with biomedical research. Academic Roles: Associate Professor (Onsager Fellow - AI and Health) Departments: Public Health and Nursing, Circulation and Medical Imaging Collaborations: National Network for Breast Cancer Research, Nordic Metabolomics Society, Metabolomics Society Research Focus: Integrating machine learning with metabolomics to uncover breast cancer risk factors, treatment response, and prognostic signatures. Her work emphasizes NMR-based metabolic profiling, biobank utilization (HUNT2, UK Biobank), and sample handling standardization (freeze-thaw cycles, delayed centrifugation). Publication Trends: Over 15 recent articles focus on metabolomic-biomarker discovery for breast cancer, circadian exercise effects, and technical validation of NMR platforms. Key subfields include lipoprotein associations, multi-omics integration, and AI-driven analysis of spatial -omics data. Scientific Contributions: Onsager Fellowship in AI and Health Key collaborator in HUNT biobank studies International presentations at Metabolomics Society conferences Developer of machine learning frameworks for multi-cancer profiling Outreach Activities: Regularly participates in public science events like Researchers' Night, delivers lectures on AI and molecular epidemiology, and contributes to popular science communication through initiatives like 'Tumorteltet'.

• Metabolomics
• Machine Learning
• Breast Cancer
• +3 more

Manuela Karola Zucknick is a Professor in the Department of Biostatistics at the University of Oslo , where she leads statistical learning research for translational and clinical cancer applications. Her work focuses on integrating multi-omics data for personalized cancer therapies, predicting drug responses, and modeling prognosis. Director of Oslo Centre for Biostatistics and Epidemiology (2023–present) Professor (2022–present) and Associate Professor (2015–2022) at University of Oslo Research Interests span high-dimensional statistical modeling, Bayesian methods for heterogeneous data integration, regularization techniques, and applications in pharmacogenomics. She develops tools for drug combination screens, survival modeling, and risk prediction incorporating prior biological knowledge. Key domains: Biostatistics, Integrative Genomics, Precision Medicine Methodological focus: Bayesian structured variable selection, Penalized Regression Publications demonstrate expertise in pan-cancer transcriptomics, proteomics for pregnancy complications, and machine learning for DNA methylation analysis. Recent work includes Tutorial on Survival Modeling (2024) and Dose-Response Prediction (2023) applied to pharmacogenomic datasets. Collaborations span clinical trials in colorectal cancer nutrition, prostate cancer screening for Lynch syndrome patients, and chronic pain research using molecular profiling.

• Biostatistics
• Cancer Research
• Risk Prediction
• +4 more

Gunnar Morken is a Professor and senior consultant in psychiatry at the Norwegian University of Science and Technology (NTNU), affiliated with the Department of Mental Health within the Faculty of Medicine and Health Sciences, and the Department of Psychiatry at St. Olavs University Hospital in Trondheim. His research focuses on bipolar disorder, schizophrenia, and psychiatric services, with an emphasis on genetic, environmental, and clinical factors influencing mental health outcomes. He leads studies on chronotherapy, sleep disturbances, and exercise interventions in mental disorders. Key research areas include the genetic architecture of bipolar disorder and suicide, the role of solar insolation in clinical outcomes, and the efficacy of light environment modifications in psychiatric care. He collaborates on randomized controlled trials evaluating digital therapies for insomnia and strength training programs for patients with schizophrenia. His work bridges clinical practice with translational research, aiming to improve patient care through evidence-based interventions. Publications highlight contributions to genomic studies, circadian rhythm analysis, and multi-omics approaches to lithium response in bipolar disorder. His interdisciplinary approach integrates neuroimmunology, pharmacogenomics, and environmental science to address complex psychiatric conditions.

• Bipolar Disorder
• Schizophrenia
• Psychiatric Services
• +5 more

Kajsa Møllersen is an Associate Professor at the Department of Computer Science, UiT The Arctic University of Norway. Her research focuses on statistical approaches to image analysis and machine learning in medical and biological contexts. Research Interests: Histopathological image analysis, divergence functions, multi-instance learning, melanoma detection, breast cancer diagnostics Teaching Roles: PhD course leader in Statistical Models, Master's lecturer in Biostatistics, Medical Statistics, and research methodology Recent publications highlight trends in applying machine learning to histopathology, omics data integration, and educational innovations in statistical pedagogy. She actively contributes to research on data sharing ethics and gender diversity in STEM fields. Key Collaborations: Computational Pathology Group, Systems Epidemiology, Realfagsdidaktikk i høyere utdanning Outreach: Public data ethics discussions, teaching methodology research, and medical informatics dissemination

• Biostatistics
• Machine Learning
• Image Analysis
• +4 more