2022 Alberto Zenere, Olof Rundquist, Mika Gustafsson, Claudio Altafini (2022) Multi-omics protein-coding units as massively parallel Bayesian networks: Empirical validation of causality structure iScience , Vol. 25 Continue to DOI Olof Rundquist, Colm Nestor, Maria Jenmalm, Sandra Hellberg, Mika Gustafsson (2022) Progesterone Inhibits the Establishment of Activation-Associated Chromatin During T(H)1 Differentiation Frontiers in Immunology , Vol. 13 Continue to DOI Rasmus Magnusson, Jesper N. Tegner, Mika Gustafsson (2022) Deep neural network prediction of genome-wide transcriptome signatures - beyond the Black-box npj Systems Biology and Applications , Vol. 8 Continue to DOI Alberto Zenere, Olof Rundquist, Mika Gustafsson, Claudio Altafini (2022) Using high-throughput multi-omics data to investigate structural balance in elementary gene regulatory network motifs Bioinformatics , Vol. 38 , s. 173-178 Continue to DOI 2021 Tejaswi Badam, Sandra Hellberg, Ratnesh Bhai Mehta, Jeannette Lechner-Scott, Rodney A. Lea, Jorg Tost, Xavier Mariette, Judit Svensson Arvelund, Colm Nestor, Mikael Benson, Göran Berg, Maria Jenmalm, Mika Gustafsson, Jan Ernerudh (2021) CD4(+) T-cell DNA methylation changes during pregnancy significantly correlate with disease-associated methylation changes in autoimmune diseases Epigenetics Continue to DOI
Alberto Zenere, Olof Rundquist, Mika Gustafsson, Claudio Altafini (2022) Multi-omics protein-coding units as massively parallel Bayesian networks: Empirical validation of causality structure iScience , Vol. 25 Continue to DOI
Olof Rundquist, Colm Nestor, Maria Jenmalm, Sandra Hellberg, Mika Gustafsson (2022) Progesterone Inhibits the Establishment of Activation-Associated Chromatin During T(H)1 Differentiation Frontiers in Immunology , Vol. 13 Continue to DOI
Rasmus Magnusson, Jesper N. Tegner, Mika Gustafsson (2022) Deep neural network prediction of genome-wide transcriptome signatures - beyond the Black-box npj Systems Biology and Applications , Vol. 8 Continue to DOI
Alberto Zenere, Olof Rundquist, Mika Gustafsson, Claudio Altafini (2022) Using high-throughput multi-omics data to investigate structural balance in elementary gene regulatory network motifs Bioinformatics , Vol. 38 , s. 173-178 Continue to DOI
Tejaswi Badam, Sandra Hellberg, Ratnesh Bhai Mehta, Jeannette Lechner-Scott, Rodney A. Lea, Jorg Tost, Xavier Mariette, Judit Svensson Arvelund, Colm Nestor, Mikael Benson, Göran Berg, Maria Jenmalm, Mika Gustafsson, Jan Ernerudh (2021) CD4(+) T-cell DNA methylation changes during pregnancy significantly correlate with disease-associated methylation changes in autoimmune diseases Epigenetics Continue to DOI
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. Genetic analysis makes diagnosis before disease breaks out Many patients with serious diseases are not helped by their medications because treatment is started too late. An international research team led from LiU is launching a unique strategy for discovering a disease progression in its earliest phase. Show all Show less