Peer-reviewed research, case studies and collaborations showing how our disease models are built, tested and used, by our own team and by independent researchers.
FeaturedPublication · 2026
Researchers used Mavatar Discovery to map ADAM17 across human lung cell types, placing an RSV mechanism in the right cells before moving to the bench.
Meineke et al., Journal of Virology
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Collaboration
2026
Bruno Paiva’s group at the University of Navarra uses Mavatar Discovery to check whether genes found in their AML cohorts hold up in large-scale public data, and to map how candidate markers connect to other genes in the disease.
Collaboration · AML
Read announcementCase study
Five Alzheimer's risk genes on different chromosomes form one connected network, linked to lysosomes and neuroinflammation.
Mavatar R&D · Alzheimer's disease
Collaboration
2026
Mavatar's contribution to the national project on precision medicine in IBD, funded by Vinnova. Delivered an integrated transcriptomic resource for Crohn’s disease and ulcerative colitis research.
Collaboration · IBD
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Mapped the biology around ABCA3 and found seven known drug targets as possible repurposing candidates.
Mavatar R&D · Rare lung disease
Read case study (opens in a new tab)Founding research
2024
Cell-type-specific network models of inflammatory disease, combining expression changes, cell-to-cell signalling and drug-target proximity, ranked thousands of drugs. Prioritised drugs were validated in vitro and in vivo in a mouse model of arthritis.
Schäfer et al., Genome Medicine
Read paper (opens in a new tab)Founding research
2019
Network-based prioritisation identified bezafibrate, which reduced arthritis severity in vivo.
Gawel et al., Genome Medicine
Read paper (opens in a new tab)Founding research
2019
The peer-reviewed foundation of the network methodology behind Mavatar's disease models. A precursor to DINA helped explain why African green monkeys avoid AIDS progression despite lifelong SIV infection, pointing to wound-healing programs that protect mucosal tissue.
Barrenäs et al., Nature Communications
Read paper (opens in a new tab)Mavatar Discovery enabled us to develop a biologically plausible, testable hypothesis from gene expression data. Its network analyses allowed us to systematically explore convergence across different analytical settings and informed downstream functional annotation, statistical analyses, and the design of a prospective clinical study.
Our collaboration demonstrates the value of combining clinical expertise with structured, data-driven resources. It’s encouraging to see how these tools are already informing research and opening new possibilities for more precise and effective IBD care.
Mavatar Discovery changes how we approach the early stages of a research question. Instead of relying solely on what we can generate in our own cohort, we can immediately explore how our findings relate to the broader biological landscape - across diseases, tissues and datasets. For a research group working across multiple haematological malignancies, that kind of systems-level context is genuinely valuable.