Author

Patricia Stoll

ETH Zurich

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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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Organism:human· 2 Approach:machine learning (classical)· 2

Frequent Co-Authors

Jan KomorowskiiD· 2 shared Klev DiamantiiD· 2 shared Aleksander Øhrn· 1 shared Alva AnnettiD· 1 shared Fredrik BarrenäsiD· 1 shared Jennifer R. S. MeadowsiD· 1 shared Karolina SmolińskaiD· 1 shared Lars FeukiD· 1 shared

Activity by Year

2020-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersBioinformatics

Jan KomorowskiORCID

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2 papers

Recent·Most cited

Interpretable machine learning identifies paediatric Systemic Lupus Erythematosus subtypes based on gene expression data

Sara A. Yones, Alva Annett, Patricia Stoll, Klev Diamanti, Linda Holmfeldt, Fredrik Barrenäs, Jennifer R. S. Meadows, Jan Komorowski

The paper uses rule-based machine learning to identify gene expression subtypes in paediatric SLE, revealing gene networks associated with disease activity and immune pathways.

Scientific Reports · May 6, 2022Peer-reviewedBioinformatics

R.ROSETTA: an interpretable machine learning framework

Mateusz Garbulowski, Klev Diamanti, Karolina Smolińska, Nicholas Baltzer, Patricia Stoll, Susanne Bornelöv, Aleksander Øhrn, Lars Feuk, Jan Komorowski

R.ROSETTA is an interpretable machine learning framework that identifies biological semantics from omics data, revealing potential gene interdependencies in autism case-control studies.

bioRxiv · Jun 10, 2020 · PreprintBioinformatics