Author

D. J. Lawson

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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Evolutionary Biology· 2 Physiology· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Approach:statistical model· 2 Assay:whole-genome sequencing· 2 Research Focus:chromatin & epigenetic regulation· 1 Research Focus:drug response / drug discovery· 1 Assay:electronic health records· 1 Approach:machine learning (classical)· 1 Research Focus:polygenic risk· 1

Frequent Co-Authors

A. K. N. Iversen· 2 shared A. Pearson· 2 shared A. Ramsoe· 2 shared A. Refoyo-Martinez· 2 shared E. Willerslev· 2 shared F. Demeter· 2 shared G. Scorrano· 2 shared K. Kristiansen· 2 shared

Activity by Year

2019-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersEvolutionary Biology

A. K. N. Iversen

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Elevated genetic risk for multiple sclerosis originated in Steppe Pastoralist populations

W. Barrie, Y. Yang, E. Irving-Pease, K. E. Attfield, G. Scorrano, L. T. Jensen, A. P. Armen, E. A. Dimopoulos, A. Stern, A. Refoyo-Martinez, A. Pearson, A. Ramsoe, C. Gaunitz, F. Demeter, M. L. S. Jorkov, , E. Willerslev

This study identifies the origin of genetic risk variants for multiple sclerosis in Steppe pastoralist populations and traces their spread into Europe through migration.

bioRxiv · Sep 23, 2022Evolutionary Biology

The Selection Landscape and Genetic Legacy of Ancient Eurasians

E. K. Irving-Pease, A. Refoyo-Martinez, W. Barrie, A. Ingason, A. Pearson, A. Fischer, K.-G. Sjögren, A. S. Halgren, R. Macleod, F. Demeter, R. A. Henriksen, T. Vimala, H. McColl, A. Vaughn, L. Speidel, , E. Willerslev

This study models ancient Eurasian selection landscapes and shows ancestry-specific genetic risk associations with modern phenotypes.

bioRxiv · Sep 22, 2022Evolutionary Biology

Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic health care data from MIMIC-III and Bristol, UK.

C. McWilliams, D. J. Lawson, R. Santos-Rodriguez, I. D. Gilchrist, A. Champneys, T. H. Gould, M. J. C. Thomas, C. P. Bourdeaux

The paper develops a machine learning tool to predict ICU discharge readiness using electronic health records from MIMIC-III and Bristol, showing improved performance over traditional criteria.

bioRxiv · Feb 5, 2019Physiology