Mika Gustafsson

Publications

2025

David Martinez, Thomas Hillerton, Julia Åkesson, Daniel Kling, Maria Lerm, Mika Gustafsson (2025) Precise and interpretable neural networks reveal epigenetic signatures of aging across youth in health and disease FRONTIERS IN AGING, Vol. 5, Article 1526146 (Article in journal) Continue to DOI

Lovisa Karlsson, Isabelle Öhrnberg, Shumaila Sayyab, David Martinez, Mika Gustafsson, Patricia Espinoza, Melissa Mendez-Aranda, Cesar Ugarte-Gil, Lameck Diero, Ronald Tonui, Jakob Paues, Maria Lerm (2025) A DNA Methylation Signature From Buccal Swabs to Identify Tuberculosis Infection Journal of Infectious Diseases, Vol. 231, p. e47-e58 (Article in journal) Continue to DOI

2024

Isabelle Öhrnberg, Lovisa Karlsson, Shumaila Sayyab, Jakob Paues, David Martinez, Mika Gustafsson, Patricia Espinoza-Lopez, Melissa Mendez-Aranda, Ericka Meza, Cesar Ugarte-Gil, Nicholas Kiprotich, Lameck Diero, Ronald Tonui, Maria Lerm (2024) A DNA methylation signature identified in the buccal mucosa reflecting active tuberculosis is changing during tuberculosis treatment Scientific Reports, Vol. 14, Article 29552 (Article in journal) Continue to DOI

Hendrik Arnold de Weerd, Dimitri Guala, Mika Gustafsson, Jane Synnergren, Jesper Tegne, Zelmina Lubovac-Pilav, Rasmus Magnusson (2024) Latent space arithmetic on data embeddings from healthy multi-tissue human RNA-seq decodes disease modules PATTERNS, Vol. 5, Article 101093 (Article in journal) Continue to DOI

Majid Pahlevan Kakhki, Antonino Giordano, Chiara Starvaggi Cucuzza, Tejaswi Badam, Samudyata Samudyata, Marianne Victoria Lemee, Pernilla Stridh, Asimenia Gkogka, Klementy Shchetynsky, Adil Harroud, Alexandra Gyllenberg, Yun Liu, Sanjaykumar Boddul, Tojo James, Melissa Sorosina, Massimo Filippi, Federica Esposito, Fredrik Wermeling, Mika Gustafsson, Patrizia Casaccia, Jan Hillert, Tomas Olsson, Ingrid Kockum, Carl M. Sellgren, Christelle Golzio, Lara Kular, Maja Jagodic (2024) A genetic-epigenetic interplay at 1q21.1 locus underlies CHD1L-mediated vulnerability to primary progressive multiple sclerosis Nature Communications, Vol. 15, Article 6419 (Article in journal) Continue to DOI

News

10-vr-grants-to-researchers-at-ifm-

Two of the grants were starting grants and eight were research project grants.

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.

Digital twins – an aid to tailor medication to individual patients

Advanced computer models of diseases can be used to improve diagnosis and treatment. The goal is to develop the models to “digital twins” of individual patients. Those twins may help to computationally identify and try the best medication.

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.

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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

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