Rebecka Jörnsten
Rebecka Jörnsten is a Professor in mathematical statistics. Her research interests include model selection, clustering and data integration in systems biology. She is active in several collaborative projects. Together with the Nelander lab, SciLife, Uppsala University, she develops large-scale network models for human cancer. She is also working with scientists at Sahlgrenska academy, Centre for brain repair, investigating how music can be used for rehabilitation and therapy.
For more information, please visit http://www.math.chalmers.se/~jornsten
Showing 30 publications
Flexible, non-parametric modeling using regularized neural networks
On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity
Modeling glioblastoma heterogeneity as a dynamic network of cell states
Non-linear, sparse dimensionality reduction via path lasso penalized autoencoders
DSAVE: Detection of misclassified cells in single-cell RNA-Seq data
Integrative discovery of treatments for high-risk neuroblastoma
Digital twins to personalize medicine
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
The cancer genome atlas pan-cancer analysis project
Erratum: Music structure determines heart rate variability of singers.
Music structure determines heart rate variability of singers
Searching for Synergies: Matrix Algebraic Approaches for Efficient Pair Screening
Chronological Changes in MicroRNA Expression in the Developing Human Brain
System-scale network modeling of cancer using EPoC
Transcriptional and metabolic data integration and modeling for identification of active pathways
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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Showing 5 research projects
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