Author

Fred Roosta

The University of QueenslandORCID 0000-0002-6920-7072Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:deep learning· 2 Research Focus:gene regulatory network inference· 2 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Assay:imaging· 1

Frequent Co-Authors

Maciej TrzaskowskiiD· 3 shared Samual MacDonaldiD· 2 shared Alberta A. H. J. ThiadensiD· 1 shared Alex William HewittiD· 1 shared Alexander Karl-Georg SchusteriD· 1 shared Alicia SchulzeiD· 1 shared Amy E. McCart ReediD· 1 shared Anke WesseliusiD· 1 shared

Activity by Year

2023-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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3 shared papersBioinformatics

Maciej TrzaskowskiORCID

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Author profile →

3 papers

Recent·Most cited

Genetic discovery using deep learning-derived optic nerve integrity phenotypes

Asma M. Aman, Eslam Zaher, Santiago Díaz‐Torres, Sjoerd J. Driessen, Victor A. de Vries, Frank C. T. van der Heide, Antonia Kolovos, Joshua M. Schmidt, Henry N. Marshall, Lania Saleh, Alicia Schulze, Gabriëlla A.M. Blokland, Carroll A.B. Webers, Carla Kallen, Anke Wesselius, , Puya Gharahkhani

The paper uses AI-derived retinal thickness phenotypes from 2D images to identify novel genetic loci for neurodegenerative diseases.

medRxiv · Sep 10, 2026 · PreprintGenetic & Genomic Medicine

STimage: Robust, confident and interpretable models for predicting gene markers from cancer histopathological images

Xiao Tan, Onkar Mulay, Jacky Xie, Samual MacDonald, Taehyun Kim, Chenhao Zhou, Zherui Xiong, Samuel X. Tan, Nan Ye, Amy E. McCart Reed, Kiarash Khosrotehrani, Fred Roosta, Maciej Trzaskowski, Peter T. Simpson, Quan Hoang Nguyen

STimage predicts gene expression and classifies cell types from histopathological images, improving robustness and interpretability for cancer and disease analysis.

Nature Communications · Jan 16, 2026Peer-reviewedBioinformatics

Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology

Samual MacDonald, Helena Foley, Melvyn Yap, Rebecca L. Johnston, Kaiah Steven, Lambros T. Koufariotis, Sowmya Sharma, Scott Wood, Venkateswar Addala, John V. Pearson, Fred Roosta, Nicola Waddell, Olga Kondrashova, Maciej Trzaskowski

This paper benchmarks Bayesian deep learning models for predicting cancer of unknown primary, showing improved uncertainty generalisation and accuracy under distributional shifts.

Scientific Reports · May 6, 2023Peer-reviewedBioinformatics