Author

Franziska Kistner

Charité – Universitätsmedizin BerlinORCID 0009-0009-8080-8643Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Assay:proteomics· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1 Organism:E. coli· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Justus L. GroßmanniD· 1 shared Lennart MartensiD· 1 shared Ludwig Roman SinniD· 1 shared Tanja HolsteiniD· 1 shared Thilo MuthiD· 1 shared Vadim DemicheviD· 1 shared

Activity by Year

2023-2023 · peak 2/yr

Co-Author Collaboration Network

6 top collaborators · 6 mutual co-author links

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1 shared paperBioinformatics

Justus L. GroßmannORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintJun 20, 2023

✦ In-depth digest

QuantUMS: uncertainty minimisation enables confident quantification in proteomics

Franziska Kistner, Justus L. Großmann, Ludwig Roman Sinn, Vadim Demichev

QuantUMS, a machine-learning module in DIA-NN, weights MS1 and fragment signals by quality-derived variance estimates, improving DIA precision, removing ratio compression in high-accuracy mode, and scoring each quantity's reliability.

50 citations

humanE. coli

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BioinformaticsBioinformaticsPeer-reviewedMay 1, 2023

PepGM: A probabilistic graphical model for taxonomic inference of viral proteome samples with associated confidence scores

Tanja Holstein, Franziska Kistner, Lennart Martens, Thilo Muth

PepGM infers viral taxonomy from proteomic data with confidence scores, showing strain-level resolution and accurate species-level assignments in some cases.

9 citations

virusproteomics

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