Author

Urszula Czerwińska

InsermORCID 0000-0002-5244-0708Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Genomics· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Assay:bulk RNA-seq· 2 Research Focus:database / resource· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1 Research Focus:dimensionality reduction· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Andrei Y. ZinovyeviD· 3 shared Emmanuel BarillotiD· 2 shared Laura CantiniiD· 2 shared Alessandro GrecoiD· 1 shared Anke K. Wienecke-BaldacchinoiD· 1 shared Arnaud MulleriD· 1 shared Askhat MolkenoviD· 1 shared Dima L. ShepelyanskyiD· 1 shared

Activity by Year

2017-2020 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 23 mutual co-author links

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3 shared papersSystems Biology

Andrei Y. ZinovyevORCID

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3 papers

Recent·Most cited

Collective intelligence defines biological functions in Wikipedia as communities in the hidden protein connection network

Andrei Y. Zinovyev, Urszula Czerwińska, Laura Cantini, Emmanuel Barillot, Klaus M. Frahm, Dima L. Shepelyansky

The paper analyzes Wikipedia's protein functional network using the REGOMAX algorithm to identify evolving hidden communities of biological functions.

PLoS Computational Biology · Feb 18, 2020Peer-reviewedSystems Biology

Independent component analysis provides clinically relevant insights into the biology of melanoma patients

Petr V. Nazarov, Anke K. Wienecke-Baldacchino, Andrei Y. Zinovyev, Urszula Czerwińska, Arnaud Muller, Dorothée Nashan, Gunnar A. G. Dittmar, Francisco J Azuaje, Stephanie Kreis

The paper uses independent component analysis to analyze melanoma transcriptomic data, distinguishing biological signals from technical biases and identifying clinically relevant patient characteristics.

bioRxiv · Aug 20, 2018 · PreprintGenomics

Determining the optimal number of independent components for reproducible transcriptomic data analysis

Ulykbek Kairov, Laura Cantini, Alessandro Greco, Askhat Molkenov, Urszula Czerwińska, Emmanuel Barillot, Andrei Y. Zinovyev

This paper introduces a method to determine the optimal number of independent components for reproducible transcriptomic data analysis using ICA stability rankings.

BMC Genomics · Sep 11, 2017Peer-reviewedBioinformatics