Author

I. Barr

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biophysics· 1 Evolutionary Biology· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Approach:Bayesian inference· 1 Research Focus:differential expression· 1 Assay:DNA methylation· 1 Research Focus:drug response / drug discovery· 1 Research Focus:host-pathogen & immune response· 1 Organism:human· 1 Assay:proteomics· 1

Frequent Co-Authors

A. K. Wheatley· 1 shared B. Ermetal· 1 shared D. E. Wentworth· 1 shared D. J. Price· 1 shared H. Hasegawa· 1 shared J. A. Juno· 1 shared J. Huddleston· 1 shared J. R. Barnes· 1 shared

Activity by Year

2020-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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Curved chords = mutual co-authorship

1 shared paperImmunology

A. K. Wheatley

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Differential cross-reactivity to the influenza B virus haemagglutinin underpins lineage-specific susceptibility between birth cohorts

P. Edler, L. S. U. Schwab, M. Aban, M. Wille, N. Spirason, Y.-M. Deng, M. A. Carlock, T. Ross, J. A. Juno, S. Rockman, A. K. Wheatley, S. J. Kent, I. Barr, D. J. Price, M. Koutsakos

The paper shows that early life exposure to influenza B virus influences cross-reactivity and susceptibility to future isolates, explaining lineage-specific infection patterns across birth cohorts.

bioRxiv · Aug 25, 2023 · PreprintImmunology

A Bayesian Analysis of Steady-State Enzyme Data leads to Estimates of Rate Constants and Uncertainties in a Multi-Step Reaction

I. Barr

This paper estimates microscopic rate constants and their uncertainties from steady-state enzyme data using Bayesian methods, improving upon traditional optimization approaches.

bioRxiv · Aug 4, 2021 · PreprintBiophysics

Integrating genotypes and phenotypes improves long-term forecasts of seasonal influenza A/H3N2 evolution

J. Huddleston, J. R. Barnes, T. Rowe, R. Kondor, D. E. Wentworth, L. Whittaker, B. Ermetal, R. S. Daniels, J. W. McCauley, S. Fujisaki, K. Nakamura, N. Kishida, S. Watanabe, H. Hasegawa, I. Barr, , T. Bedford

This study develops a forecasting framework for influenza A/H3N2 evolution by integrating phenotypic antigenic drift data with sequence-based fitness estimates, showing improved long-term predictions.

bioRxiv · Jun 12, 2020Evolutionary Biology