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Ørnulf Borgan

Ørnulf Borgan

D-Index & Metrics

Mathematics

D-Index
35
Citations
6167
World Ranking
2743
National Ranking
20

Overview

Ørnulf Borgan is affiliated with the University of Oslo in Norway, where their research spans across multiple interdisciplinary fields. Their work primarily focuses on the integration of statistical methods with biological and environmental sciences.

The scientist's main fields of study include:

  • Mathematics
  • Biochemistry, Genetics and Molecular Biology

Within these areas, their research contributions delve into several subfields such as:

  • Statistics and Probability
  • Molecular Biology
  • Building and Construction
  • Geochemistry and Petrology
  • Artificial Intelligence

Their work consistently addresses topics including:

  • Statistical Methods and Inference
  • Gene expression and cancer classification
  • Statistical Methods and Bayesian Inference
  • Bioinformatics and Genomic Networks
  • Recycling and utilization of industrial and municipal waste in materials production
  • Coal and Its By-products
  • Aquaculture disease management and microbiota

Among notable recent publications attributed to them are:

  • Metagenomic Shotgun Analyses Reveal Complex Patterns of Intra- and Interspecific Variation in the Intestinal Microbiomes of Codfishes, 2020, Applied and Environmental Microbiology
  • Variation in chemical composition of MSWI fly ash and dry scrubber residues, 2021, Waste Management
  • Continuous and discrete-time survival prediction with neural networks, 2021, Lifetime Data Analysis
  • DNA copy number motifs are strong and independent predictors of survival in breast cancer, 2020, Communications Biology
  • Tailored graphical lasso for data integration in gene network reconstruction, 2021, BMC Bioinformatics

Ørnulf Borgan has frequently published in venues including:

  • Scandinavian Journal of Statistics
  • Applied and Environmental Microbiology
  • Waste Management
  • Lifetime Data Analysis
  • Communications Biology

Frequent collaborators in their research include:

  • Ingrid K. Glad
  • Camilla Lingjærde
  • Tonje G. Lien
  • Eirik Nøst Nedkvitne
  • Dag Øistein Eriksen

The scientist's research portfolio shows a blend of advanced quantitative methods applied to biological data, environmental issues, and materials science. The intersection of mathematics, molecular biology, and artificial intelligence frames much of their recent investigative focus.

Best Publications

  • Statistical Models Based on Counting Processes

    Frank Coolen

  • Predicting survival from microarray data—a comparative study

    H.M. Bøvelstad;S. Nygård;H.L. Størvold;M. Aldrin

  • Exposure stratified case-cohort designs.

    Ornulf Borgan;Bryan Langholz;Sven Ove Samuelsen;Larry Goldstein

  • Counting process models for life history data: a review

    Per Kragh Andersen;Ørnulf Borgan

  • Time-to-Event Prediction with Neural Networks and Cox Regression

    Håvard Kvamme;Ørnulf Borgan;Ida Scheel

  • Linear Nonparametric Tests for Comparison of Counting Processes, with Applications to Censored Survival Data, Correspondent Paper

    Per Kragh Andersen;Ørnulf Borgan;Richard Gill;Niels Keiding

  • A method for checking regression models in survival analysis based on the risk score.

    Jon Ketil Grønnesby;Ørnulf Borgan

  • Methods for the Analysis of Sampled Cohort Data in the Cox Proportional Hazards Model

    Ørnulf Borgan;L. Goldstein;Bryan Langholz

  • Counter-matching: A stratified nested case-control sampling method

    Bryan Langholz;Ørnulf Borgan

  • Maximum likelihood estimation in parametric counting process models, with applications to censored failure time data

    O. Borgan

  • A note on confidence intervals and bands for the survival function based on transformations

    Ø. Borgan;K. Liestøl

  • Censoring, truncation and filtering in statistical models based on counting processes

    P. K. Andersen;O. Borgan;Richard David Gill;N. Keiding

  • The Statistical Analysis of Failure Time Data

    Ørnulf Borgan

  • Survival prediction from clinico-genomic models - a comparative study

    Hege M Bøvelstad;Ståle Nygård;Ståle Nygård;Ørnulf Borgan

  • Survival models and martingale dynamics: original text, discussion and reply

    Elja Arjas;N. Keiding;O. Borgan;P. K. Andersen

  • Continuous and discrete-time survival prediction with neural networks.

    Håvard Kvamme;Ørnulf Borgan

  • Estimation of absolute risk from nested case-control data.

    Bryan Langholz;Ørnulf Borgan

  • Confidence intervals and confidence bands for the cumulative hazard rate function and their small sample properties

    Ole Bie;Ørnulf Borgan;Knut Liestøl

  • Covariate adjustment of event histories estimated from Markov chains: the additive approach.

    Odd O. Aalen;Ørnulf Borgan;Harald Fekjær

  • Dynamic analysis of multivariate failure time data.

    Odd O. Aalen;Johan Fosen;Harald Weedon-Fekjær;Ørnulf Borgan

Frequent Co-Authors

Odd O. Aalen
Odd O. Aalen University of Oslo
Per Kragh Andersen
Per Kragh Andersen University of Copenhagen
Niels Keiding
Niels Keiding University of Copenhagen
Richard D. Gill
Richard D. Gill Leiden University
Anne Lise Børresen-Dale
Anne Lise Børresen-Dale Oslo University Hospital
Kjetill S. Jakobsen
Kjetill S. Jakobsen University of Oslo
Jan M. Hoem
Jan M. Hoem Max Planck Society
Peter Van Loo
Peter Van Loo The Francis Crick Institute
David C. Wedge
David C. Wedge University of Manchester
Terje R. Pedersen
Terje R. Pedersen Oslo University Hospital

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