Alzheimer’s research is often constrained by:

- Multi-factorial disease biology

- Strong heterogeneity across patient populations

- Fragmented transcriptomic data across tissues and disease stages

- Limited translatability from single models or cohorts

As a result, researchers often spend months finding and integrating datasets before meaningful biological interpretation can begin.

Our research platform, Mavatar Discovery, integrates thousands of public transcriptomic datasets across neurodegenerative diseases, brain regions, peripheral tissues, and experimental models generated over years, across multiple technologies, laboratories, and subject cohorts. This information is harmonized, processed, and contextualized, so researchers can move from raw data to insight - instantly.

Instead of working with isolated datasets or predefined hypotheses, researchers explore integrated biological networks that capture how genes behave together across multiple contexts - enabling systematic exploration of disease mechanisms and biological variation.

Just as living systems rely on collaboration to survive and thrive, genes never work in isolation. They operate in coordinated groups, adapting their roles depending on context, condition, and environment.

Gene co-expression networks reveal the hidden architecture of life. Instead of studying genes one by one, they show how genes work together as coordinated systems - forming functional modules that drive biological processes, development, and disease.

By analyzing patterns of gene activity across thousands of samples, these networks uncover relationships traditional approaches miss. Genes that rise and fall together are often part of the same pathways, regulated by shared mechanisms, or contributing to the same phenotypes.

Gene co-expression networks transform massive transcriptomic datasets into intuitive, interpretable maps of biology.

What emerges is not noise - but structure, logic, and biological meaning.

Explore systems-level disease mechanisms
Move beyond single-gene analysis by investigating co-expression networks and coordinated biological pathways involved in neurodegeneration.

Compare Alzheimer’s to related conditions
Explore biological overlap with neurodegenerative and mental disorders to identify shared and distinct mechanisms.

Accelerate hypothesis generation
Start with biologically meaningful networks already in place, supporting faster hypothesis development and more informed study design.

For Academic & Translational Researchers

- Faster hypothesis generation

- Deeper biological context for complex mechanisms

- Stronger pilot data for grants

- Less dependence on custom bioinformatics pipelines

For Pharma & R&D Teams

- Earlier insight into disease biology

- Improved target and biomarker exploration

- Reduced early-stage uncertainty

- Scalable, consistent analysis across programs