Mika Gustafsson

Publications

2024

Samuel Schäfer, Martin Smelik, Oleg Sysoev, Yelin Zhao, Desiré Eklund, Sandra Lilja, Mika Gustafsson, Holger Heyn, Antonio Julia, Istvan A. Kovacs, Joseph Loscalzo, Sara Marsal, Huan Zhang, Xinxiu Li, Danuta Gawel, Hui Wang, Mikael Benson (2024) scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases Genome Medicine, Vol. 16, Article 42 Continue to DOI

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, Data for: Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis, Vol. 14, Article 6903 Continue to DOI

Alberto Zenere, Sandra Hellberg, Georgia Papapavlou Lingehed, Maria Svenvik, Johan Mellergård, Charlotte Dahle, Magnus Vrethem, Johanna Raffetseder, Mohsen Khademi, Tomas Olsson, Marie Blomberg, Maria Jenmalm, Claudio Altafini, Mika Gustafsson, Jan Ernerudh (2023) Prominent epigenetic and transcriptomic changes in CD4(+) and CD8(+) T cells during and after pregnancy in women with multiple sclerosis and controls Journal of Neuroinflammation, Vol. 20, Article 98 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, Vol. 24, Article bbad293 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

News

Severe MS predicted using machine learning

A combination of only 11 proteins can predict long-term disability outcomes in multiple sclerosis (MS) for different individuals. The proteins could be used to tailor treatments to the individual based on the expected severity of the disease.

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

Share on

Facebook X LinkedIn Email