The biological intelligence behind your next R&D decision
You bring the biology question. Our scientists bring the disease models, the data and answers you can act on.
THE CHALLENGE
Most programs fail on biology, not chemistry
A target looks strong in one dataset, then falls apart in the next cohort. A biomarker scores well once and fails validation. A trial misses because the patient population was several diseases, not one. These are biology questions, and they’re cheapest to answer before the program commits.
- Target risk
- Biomarker failure
- Patient heterogeneity
OUR SERVICES
Five questions we answer for drug programs
Each project starts from one decision your team needs to make. You don’t need an in-house computational team for any of them.
Know if your target holds up before you commit the program. We assess whether your target is central to the disease or peripheral, which tissues and cell types the signal comes from, and whether it replicates across independent patient cohorts. You receive a documented assessment with the full reasoning behind it, covering network context, key pathways, cell-type resolution and independent supporting evidence, built to stand up to an advisory board or a partner’s due diligence.
Biomarkers built to survive the validation cohort. We look for candidates that hold up in a different cohort and on a different platform, and that are biologically meaningful, measurable in accessible tissue and practical to assay. You receive a short list with the reasoning for each candidate, tested across independent datasets before you go to the lab, instead of a ranked spreadsheet of hundreds of genes.
The responders may already be in your data. We explore whether your population is one group or several, and what biology separates likely responders from non-responders. You receive molecular subgroups with the biology behind each, as input for trial design and enrollment. This is exploratory work: the results are hypotheses to test, not validated stratification tools.
Your drug may already work somewhere you haven’t looked. We map where your target’s biology appears across other diseases and which conditions share the mechanism your drug acts on. You receive a prioritized list of candidate indications, each with its molecular rationale, as hypotheses to confirm experimentally.
You’ve generated the data, and we put it in context. We interpret your differentially expressed genes and findings against millions of public gene-expression profiles across diseases, tissues and studies. You receive prioritized pathways and a biological narrative your team can act on.
Understanding the underlying biology of rare and immune-mediated conditions is essential for advancing research in the field. Through this collaboration, we aim to further strengthen our scientific knowledge base using structured, data-driven analyses.
SCIENTIFIC INTERPRETATION
A scientist reads and reviews every result before you do
Bioinformatics gives you a network, a gene list or a score. It doesn’t tell you which result is biology and which is an artifact, or what it means for your program. Before anything reaches you, a Mavatar specialized scientist checks it against the disease context, the data behind it and the question you asked. You get an answer you can act on, not output you have to interpret yourself.
HOW IT WORKS
From your question to a decision you can defend
Your project builds on existing data and disease models, so we start with your question, not with infrastructure. Your team keeps full scientific oversight, IP ownership and decision authority, and the right to publish jointly or independently.
We listen to your question
We define the disease, tissue, population and the decision you’re facing, plus what a useful answer looks like for your program. It’s a scientific conversation, you get a feasibility study.
We build the disease model
We select the disease model that fits your question, or build one for your specific disease, tissue or cell type.
We analyze and interpret
We run the analysis your question needs, and a scientist reviews every result before it reaches you.
You decide
You receive a documented, publication-ready deliverable: the findings, the biological reasoning, figures, methods and recommended next steps.
WHY MAVATAR
The foundation is already built
Curated database
8M+ public transcriptomes, harmonized across studies, tissues and technologies.
Built from biology, not literature
Finds relationships not yet published, in the tissue and cell type that matter.
Results you can trace
Findings must hold across independent datasets and trace back to their samples.
Scientists, not just software
Senior and computational scientists interpret every result for you.
Partners
Active collaborations
Precision medicine for IBD - RISE, Vinnova Funded
Partners: Pfizer, Takeda Pharma, Örebro Universitet, and Lunds Universitet delivering data-driven resources to identify molecular subtypes of IBD, explore predictors of treatment response, and more.
SOBI - IFNγ-driven mechanisms
Through advanced analytics of public transcriptomic data, we aim to contribute to a clearer understanding of IFNγ-driven mechanisms across relevant biological contexts.
Clínica Universidad de Navarra - AML
A research collaboration with Bruno Paiva, one of Europe's most cited researchers in hematological neoplasms to validate candidate biomarkers and explore biological relationships in AML.
Lund University - Alzheimer's Disease
Together with Prof. Tomas Deierborg’s group, we explored galectin-3 and microglial activation in Alzheimer’s disease through brain and disease-specific networks.
Partnership
Let's start with your biology question
Tell us about your program and the decision you’re facing. We come back with a tailored proposal and pre-feasibility study for your program.
Talk to our team