Rebecka Jörnsten

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Highly interconnected genes in disease-specific networks are enriched for disease-associated polymorphisms

M. Jarvelin, L. Coin, A. Ramasamy, 2012, Genome Biology.

Clustering and classification based on the L 1 data depth

Rebecka Jörnsten, 2004 .

MicroRNA expression pattern of undifferentiated and differentiated human embryonic stem cells.

L. Goff, Rebecka Jörnsten, J. Chesnut, 2007, Stem cells and development.

Network modeling of the transcriptional effects of copy number aberrations in glioblastoma

Torbjörn E. M. Nordling, C. Sander, B. Nilsson, 2011, Molecular systems biology.

DSAVE: Detection of misclassified cells in single-cell RNA-Seq data

Jonathan L. Robinson, Rebecka Jörnsten, J. Nielsen, 2020, PloS one.

Sources of variation in cell-type RNA-Seq profiles

Jonathan L. Robinson, Rebecka Jörnsten, J. Nielsen, 2020, PloS one.

Integrative discovery of treatments for high-risk neuroblastoma

Rebecka Jörnsten, S. Nelander, R. Elgendy, 2020, Nature Communications.

A Factor Analysis Approach for Clustering Patient Reported Outcomes

J. Deasy, Rebecka Jörnsten, G. Steineck, 2016, Methods of Information in Medicine.

System-scale network modeling of cancer using EPoC.

Rebecka Jörnsten, S. Nelander, T. Kling, 2012, Advances in experimental medicine and biology.

Identifying radiation-induced survivorship syndromes affecting bowel health in a cohort of gynecological cancer survivors

J. Deasy, Rebecka Jörnsten, G. Steineck, 2017, PloS one.

Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data

Jonathan L. Robinson, J. Nielsen, Rebecka Jörnsten, 2022, bioRxiv.

Microarray image compression: SLOCO and the effect of information loss

Kannan Ramchandran, Bin Yu, Rebecka Jörnsten, 2003, Signal Processing.

Dose-response relationships of the sigmoid for urgency syndrome after gynecological radiotherapy

Rebecka Jörnsten, G. Steineck, F. Sjöberg, 2018, Acta oncologica.

Searching for Synergies: Matrix Algebraic Approaches for Efficient Pair Screening

Rebecka Jörnsten, S. Nelander, T. Kling, 2013, PloS one.

Bandwidth Selection for Gaussian Kernel Ridge Regression via Jacobian Control

Rebecka Jörnsten, Oskar Allerbo, 2022, 2205.11956.

Sources of variation in cell-type RNA-Seq profiles

Jonathan L. Robinson, Rebecka Jörnsten, J. Nielsen, 2020, PloS one.

On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity

Rebecka Jörnsten, B. Kulcsár, Vincent Szolnoky, 2022, NeurIPS.

Postsynaptic Potential in CA1 Pyramidal Neurons In Vivo Demonstration of a Late Depolarizing

Barbara F. Akum, Rebecka Jörnsten, C. Rongo, 2007 .

Activity-Independent Regulation of Dendrite Patterning by Postsynaptic Density Protein PSD-95

Barbara F. Akum, Rebecka Jörnsten, C. Rongo, 2006, The Journal of Neuroscience.

Modeling glioblastoma heterogeneity as a dynamic network of cell states

Rebecka Jörnsten, S. Nelander, B. Westermark, 2021, Molecular systems biology.

Integrative Modeling Reveals Annexin A2-mediated Epigenetic Control of Mesenchymal Glioblastoma

Rebecka Jörnsten, S. Nelander, T. Kling, 2016, EBioMedicine.

Digital twins to personalize medicine

Mika Gustafsson, Oleg Sysoev, Danuta R. Gawel, 2019, Genome Medicine.

Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content

Voichita D. Marinescu, Rebecka Jörnsten, Sven Nelander, 2015, Nucleic acids research.

A 6-gene signature identifies four molecular subgroups of neuroblastoma

R. Stallings, Rebecka Jörnsten, J. Maris, 2011, Cancer Cell International.

Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancers

Rebecka Jörnsten, F. Held, Ida Larsson, 2023, bioRxiv.

Data-driven representations using deep network-coherent methylation autoencoders to identify robust disease and risk factor signatures

M. Gustafsson, Rebecka Jörnsten, D. Martínez-Enguita, 2023, bioRxiv.

DNA Methylation Changes Separate Allergic Patients from Healthy Controls and May Reflect Altered CD4+ T-Cell Population Structure

Michael A. Langston, Mika Gustafsson, Fredrik Barrenäs, 2014, PLoS genetics.

Simultaneous Gene Clustering and Subset Selection for Sample Classification Via MDL

Bin Yu, Rebecka Jörnsten, Bin Yu, 2003, Bioinform..

Color preference, seasonality, spatial distribution and species composition of thrips (Thysanoptera: Thripidae) in northern highbush blueberries

Rebecka Jörnsten, J. Barry, P. Oudemans, 2010 .

Bandwidth Selection for Gaussian Kernel Ridge Regression via Jacobian Control

Rebecka Jörnsten, Oskar Allerbo, 2022, ArXiv.

A meta-data based method for DNA microarray imputation

Ming Ouyang, Rebecka Jörnsten, Hui-Yu Wang, 2007, BMC Bioinformatics.

DNA microarray data imputation and significance analysis of differential expression

Ming Ouyang, William J. Welsh, Rebecka Jörnsten, 2005, Bioinform..

A novel role for snapin in dendrite patterning: interaction with cypin.

Barbara F. Akum, Rebecka Jörnsten, B. Firestein, 2005, Molecular biology of the cell.

A Robust Clustering Method and Visualization Tool Based on Data Depth

Rebecka Jörnsten, Cun-Hui Zhang, Yehuda Vardi, 2002 .

Integrative discovery of treatments for high-risk neuroblastoma

Rebecka Jörnsten, R. Elgendy, Michael Vanlandewijck, 2020, Nature Communications.

LASSIM—A network inference toolbox for genome-wide mechanistic modeling

Huan Zhang, Rebecka Jörnsten, Gunnar Cedersund, 2017, PLoS Comput. Biol..

LASSIM - a network inference toolbox for genome-wide mechanistic modeling

Huan Zhang, Mika Gustafsson, Torbjörn E. M. Nordling, 2017, bioRxiv.

Screening anti-inflammatory compounds in injured spinal cord with microarrays: a comparison of bioinformatics analysis approaches.

Rebecka Jörnsten, Ronald P Hart, Rebecka Jörnsten, 2004, Physiological genomics.

analysis approaches cord with microarrays: a comparison of bioinformatics Screening anti-inflammatory compounds in injured spinal

Rebecka Jörnsten, R. Hart, Jonathan Z. Pan, 2016 .

Mixture models with multiple levels, with application to the analysis of multifactor gene expression data.

Rebecka Jörnsten, Sündüz Keleş, S. Keleş, 2008, Biostatistics.

Simultaneous Model Selection via Rate-Distortion Theory, With Applications to Cluster and Significance Analysis of Gene Expression Data

Rebecka Jörnsten, Rebecka Jörnsten, 2009 .

Clustering with multiple distance metrics-mixture models with profile transformations

Rebecka Jörnsten, 2007 .

Molecular natural history of breast cancer: Leveraging transcriptomics to predict breast cancer progression and aggressiveness

Rebecka Jörnsten, J. Nielsen, Daniel J Cook, 2020, Cancer medicine.

Bioinformatic analysis of neural stem cell differentiation.

S. Keleş, L. Goff, Rebecka Jörnsten, 2007, Journal of biomolecular techniques : JBT.

Late radiation-induced bowel syndromes, tobacco smoking, age at treatment and time since treatment – gynecological cancer survivors

Rebecka Jörnsten, G. Steineck, F. Sjöberg, 2017, Acta oncologica.

Flexible, non-parametric modeling using regularized neural networks pdf

Rebecka Jörnsten, Oskar Allerbo, Rebecka Jörnsten, 2020, Computational Statistics.

Robust Neural Network Classification via Double Regularization

J. Jonasson, Rebecka Jörnsten, Olof Zetterqvist, 2021, ArXiv.

Learning with noisy labels

J. Jonasson, Rebecka Jörnsten, 2018 .

E-publications@rcsi Citation Attribution-non-commercial-sharealike 1.0 a 6-gene Signature Identifies Four Molecular Subgroups of Neuroblastoma

R. Stallings, Rebecka Jörnsten, J. Maris, 2022 .

NCAE: data-driven representations using a deep network-coherent DNA methylation autoencoder identify robust disease and risk factor signatures

M. Gustafsson, Rebecka Jörnsten, D. Martínez-Enguita, 2023, Briefings Bioinform..