Rebecka Jörnsten
Rebecka Jörnsten är professor i matematisk statistik. Hennes forskningsintressen inkluderar modellval, klustring och system biologi. Hon är aktiv i flera projekt med forskare i andra discipliner. Tillsammans med Sven Nelander, SciLife, Uppsala Universitet, utvecklar hon storskaliga nätverksmodeller för cancer.
För mer information, besök den personliga hemsidan https://rjornsten.github.io/
Visar 39 publikationer
Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancers
GLIOBLASTOMA GROWTH IS SHAPED BY INVASION ROUTE-SPECIFIC FUNCTIONAL SIGNATURES
Flexible, non-parametric modeling using regularized neural networks
On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity
Non-linear, sparse dimensionality reduction via path lasso penalized autoencoders
Modeling glioblastoma heterogeneity as a dynamic network of cell states
DSAVE: Detection of misclassified cells in single-cell RNA-Seq data
Digital twins to personalize medicine
Integrative discovery of treatments for high-risk neuroblastoma
Sources of variation in cell-type RNA-Seq profiles
TargetTranslator: Big data identifies non-canonical targets for high risk neuroblastoma
LASSIM-A network inference toolbox for genome-wide mechanistic modeling
Integrative Modeling Reveals Annexin A2-mediated Epigenetic Control of Mesenchymal Glioblastoma
Music structure determines heart rate variability of singers
The cancer genome atlas pan-cancer analysis project
Chronological Changes in MicroRNA Expression in the Developing Human Brain
Searching for Synergies: Matrix Algebraic Approaches for Efficient Pair Screening
Erratum: Music structure determines heart rate variability of singers.
Transcriptional and metabolic data integration and modeling for identification of active pathways
System-scale network modeling of cancer using EPoC
A 6-gene signature identifies four molecular subgroups of neuroblastoma
Network modeling of the transcriptional effects of copy number aberrations in glioblastoma
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Visar 6 forskningsprojekt
FEAT: Fordonshantering för effektiva och hållbara elekriska mikromobilitetssystem
Robustly and Optimally Controlled Training Of neural Networks II (OCTON II)
Robustly and Optimally Controlled Training Of neural Networks I (OCTON I)
Stochastics for big data and big systems - bridging local and global