Mika Gustafsson

Publications

2023

Julia Åkesson, Sara Hojjati, Sandra Hellberg, Johanna Raffetseder, Mohsen Khademi, Robert Rynkowski, Ingrid Kockum, Claudio Altafini, Zelmina Lubovac-Pilav, Johan Mellergård, Maria Jenmalm, Fredrik Piehl, Tomas Olsson, Jan Ernerudh, Mika Gustafsson (2023) Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis Nature Communications, Vol. 14 Continue to DOI

Mika Gustafsson, Jan Ernerudh, Tomas Olsson (2023) Data for: Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis

Laura Tarnawski, Vladimir S. Shavva, Eric J. Kort, Zhengbing Zhuge, Ingrid Nilsson, Alessandro L. Gallina, David Martínez-Enguita, Benjamin Heller Sahlgren, Matthew Weiland, April S. Caravaca, Staffan Schmidt, Ping Chen, Katarina Abbas, Fu-Hua Wang, Osman Ahmed, Michael Eberhardson, Anna Farnert, Eddie Weitzberg, Mika Gustafsson, Jan Kehr, Stephen G. Malin, Henrik Hult, Mattias Carlstrom, Stefan Jovinge, Peder S. Olofsson (2023) Cholinergic regulation of vascular endothelial function by human ChAT(+) T cells Proceedings of the National Academy of Sciences of the United States of America, Vol. 120, Article e2212476120 Continue to DOI

David Martinez, Sanjiv Dwivedi, Rebecka Jornsten, Mika Gustafsson (2023) NCAE: data-driven representations using a deep network-coherent DNA methylation autoencoder identify robust disease and risk factor signatures Briefings in Bioinformatics Continue to DOI

William Lövfors, Rasmus Magnusson, Cecilia Jönsson, Mika Gustafsson, Charlotta S. Olofsson, Gunnar Cedersund, Elin Nyman (2023) A comprehensive mechanistic model of adipocyte signaling with layers of confidence NPJ SYSTEMS BIOLOGY AND APPLICATIONS, Vol. 9, Article 24 Continue to DOI

News

A step towards AI-based precision medicine

AI which finds patterns in complex biological data could eventually contribute to the development of individually tailored healthcare. Researchers have developed an AI-based method applicable to various medical and biological issues.

Why women with multiple sclerosis get better when pregnant

Women suffering from multiple sclerosis temporarily get much better when pregnant. Researchers have now identified the beneficial changes naturally occurring in the immune system during pregnancy. The findings can show the way to new treatments.

Predicting the risk of severe side effects of cancer treatment

Some patients experience life-threatening side effects during cancer treatment. Researchers at LiU have developed a model that can predict which patients have a high probability of side effects.

Artificial intelligence finds disease-related genes

An artificial neural network can reveal patterns in huge amounts of gene expression data, and discover groups of disease-related genes. The scientists hope that the method can eventually be applied within precision medicine.

Predicting the severity of multiple sclerosis

Cells in the immune system of patients with multiple sclerosis behave differently from those of healthy individuals. LiU researchers have exploited this difference to develop a method that can predict disease activity in multiple sclerosis.

Show all Show less

Tags

• Translational bioinformatics
• Theory and Modelling
• Employee
• CENIIT (Centre for industrial IT)
• Artificial Intelligence
• Bioinformatics (BIOIN)
• Department of Physics, Chemistry and Biology (IFM)
• Linköping University (LIU)
• Mika Gustafsson (mikgu75)
• Professor

Share on

Facebook Twitter LinkedIn Email