Gene networks from high-throughput data: Reverse engineering and analysis. - Mika Gustafsson (2010)

LASSIM - A network inference toolbox for genome-wide mechanistic modeling. - Rasmus Magnusson; Guido Pio Mariotti; Mattias Köpsén; William Lövfors; Danuta R. Gawel; Rebecka Jörnsten; Jörg Linde; Torbjörn E. M. Nordling; Elin Nyman; Sylvie Schulze; Colm E. Nestor; Huan Zhang; et al. (PLoS Comput. Biol., 2017)

LiPLike: towards gene regulatory network predictions of high certainty. - Rasmus Magnusson; Mika Gustafsson (Bioinform., 2020)

MODifieR: an Ensemble R Package for Inference of Disease Modules from Transcriptomics Networks. - Hendrik A. de Weerd; Tejaswi V. S. Badam; David Martínez-Enguita; Julia Åkesson; Daniel Muthas; Mika Gustafsson; Zelmina Lubovac-Pilav (Bioinform., 2020)

Using high-throughput multi-omics data to investigate structural balance in elementary gene regulatory network motifs. - Alberto Zenere; Olof Rundquist; Mika Gustafsson; Claudio Altafini (Bioinform., 2021)

ComHub: Community predictions of hubs in gene regulatory networks. - Julia Åkesson; Zelmina Lubovac-Pilav; Rasmus Magnusson; Mika Gustafsson (BMC Bioinform., 2021)

NCAE: data-driven representations using a deep network-coherent DNA methylation autoencoder identify robust disease and risk factor signatures. - David Martínez-Enguita; Sanjiv K. Dwivedi; Rebecka Jörnsten; Mika Gustafsson (Briefings Bioinform., 2023)

Latent space arithmetic on data embeddings from healthy multi-tissue human RNA-seq decodes disease modules. - Hendrik A. de Weerd; Dimitri Guala; Mika Gustafsson; Jane Synnergren; Jesper Tegnér; Zelmina Lubovac-Pilav; Rasmus Magnusson (Patterns, 2024)