U
Urszula Czerwinska
Researcher at PSL Research University
Publications - 18
Citations - 422
Urszula Czerwinska is an academic researcher from PSL Research University. The author has contributed to research in topics: Interpretability & Biological network. The author has an hindex of 8, co-authored 16 publications receiving 285 citations. Previous affiliations of Urszula Czerwinska include Curie Institute & Paris Descartes University.
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Journal ArticleDOI
Adjustment of dendritic cells to the breast-cancer microenvironment is subset specific.
Paula Michea,Paula Michea,Floriane Noel,Floriane Noel,Floriane Noel,Eve Zakine,Eve Zakine,Urszula Czerwinska,Philémon Sirven,Philémon Sirven,Omar Abouzid,Omar Abouzid,Christel Goudot,Christel Goudot,Alix Scholer-Dahirel,Alix Scholer-Dahirel,Anne Vincent-Salomon,Anne Vincent-Salomon,Fabien Reyal,Fabien Reyal,Sebastian Amigorena,Sebastian Amigorena,Maude Guillot-Delost,Maude Guillot-Delost,Elodie Segura,Elodie Segura,Vassili Soumelis,Vassili Soumelis +27 more
TL;DR: The adjustment of DCs to the tumor microenvironment is subset specific and can be used to predict disease outcome, and this work provides a resource for the identification of potential targets and biomarkers that might improve antitumor therapies.
Journal ArticleDOI
Independent Component Analysis for Unraveling the Complexity of Cancer Omics Datasets.
Nicolas Sompairac,Petr V. Nazarov,Urszula Czerwinska,Urszula Czerwinska,Laura Cantini,Anne Biton,Askhat Molkenov,Zhaxybay Zhumadilov,Emmanuel Barillot,Emmanuel Barillot,François Radvanyi,François Radvanyi,Alexander N. Gorban,Ulykbek Kairov,Andrei Zinovyev,Andrei Zinovyev +15 more
TL;DR: A review of a number of recent works where ICA was shown to be a useful tool for unraveling the complexity of cancer biology from the analysis of different types of omics data, mainly collected for tumoral samples and discusses the emerging ICA applications to the integrative analysis of multi-level omics datasets.
Journal ArticleDOI
Determining the optimal number of independent components for reproducible transcriptomic data analysis
Ulykbek Kairov,Laura Cantini,A. Greco,Askhat Molkenov,Urszula Czerwinska,Emmanuel Barillot,Andrei Zinovyev +6 more
TL;DR: It is demonstrated that a sufficient number of dimensions is required for biological interpretability of the ICA decomposition and that the most stable components with ranks below MSTD have more chances to be reproduced in independent studies compared to the less stable ones.
Journal ArticleDOI
A multiscale signalling network map of innate immune response in cancer reveals cell heterogeneity signatures.
Maria Kondratova,Urszula Czerwinska,Urszula Czerwinska,Nicolas Sompairac,Nicolas Sompairac,Sebastian Amigorena,Vassili Soumelis,Emmanuel Barillot,Andrei Zinovyev,Inna Kuperstein +9 more
TL;DR: An integrated multi-scale map of signalling networks representing the different immune cells and their interactions is provided and its utility for data interpretation is shown.
Journal ArticleDOI
Deconvolution of transcriptomes and miRNomes by independent component analysis provides insights into biological processes and clinical outcomes of melanoma patients
Petr V. Nazarov,Anke Wienecke-Baldacchino,Andrei Zinovyev,Andrei Zinovyev,Urszula Czerwinska,Urszula Czerwinska,Urszula Czerwinska,Arnaud Muller,Dorothee Nashan,Gunnar Dittmar,Francisco Azuaje,Stephanie Kreis +11 more
TL;DR: A method that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large discovery datasets, and provides the prognosis of patient survival is presented.