Author

Elizabeth D. Williams

Queensland University of TechnologyORCID 0000-0002-3364-6655Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Assay:bulk RNA-seq· 1 Research Focus:cell-cell communication· 1 Research Focus:differential expression· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Alivia R. CalabreseiD· 1 shared Christian U. BlankiD· 1 shared Conrad LeonardiD· 1 shared David James WarneiD· 1 shared Jia ZhangiD· 1 shared John V. PearsoniD· 1 shared Khoa Anh TraniD· 1 shared Lambros T. KoufariotisiD· 1 shared

Activity by Year

2022-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperSystems Biology

Alivia R. CalabreseORCID

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2 papers

Recent·Most cited

Explainable machine learning identifies features and thresholds predictive of immunotherapy response

Khoa Anh Tran, Venkateswar Addala, Lambros T. Koufariotis, Jia Zhang, Scott Wood, Conrad Leonard, Lotte L. Hoeijmakers, Christian U. Blank, Mireia Crispin‐Ortuzar, Elizabeth D. Williams, Olga Kondrashova, John V. Pearson, Nicola Waddell

The paper develops a multi-omic random forest model to predict immunotherapy response in melanoma using SHAP for feature threshold identification.

Scientific Reports · May 23, 2026Peer-reviewedBioinformatics

Computationally efficient framework for diagnosing, understanding, and predicting biphasic population growth

Ryan J. Murphy, Oliver J. Maclaren, Alivia R. Calabrese, Patrick B. Thomas, David James Warne, Elizabeth D. Williams, Matthew J. Simpson

The paper develops a computationally efficient framework for diagnosing, understanding, and predicting biphasic population growth using profile likelihood analysis.

Journal of The Royal Society Interface · Dec 1, 2022Peer-reviewedSystems Biology