TECHNICAL FOUNDATION
Turning complex biology into clear, actionable insight
Mavatar is built on a uniquely powerful technological foundation that combines AI,
big data, and molecular biology to deliver next-generation precision medicine.
At the core is our proprietary framework: DINA (Deep Integrated Network Analysis).
Built on the world's molecular data
We integrate millions of publicly available transcriptomic and proteomic datasets—spanning ethnicities, disease types, treatment stages, and more.
This enables us to:
• Capture diverse, real-world biological variation
• Learn across thousands of diseases and treatment responses
• Scale globally without requiring new patient data
DINA
DINA: The Engine Elevating Precision Medicine
DINA (Deep Integrated Network Analysis) is Mavatar’s proprietary platform that decodes disease biology at an unmatched depth
A New Kind of Insight
- Purely data-driven and free from outdated assumptions
- Merges human, animal, and clinical trial data
- Reveals patterns missed by traditional research
Systems-Level Understanding
- Maps all 22,000 genes across cell types
- Uncovers the network behind disease progression
- Generates precise, clinically useful profiles
Discovery Without Borders
- Learns across diseases and tissues simultaneously
- Finds shared biology for drug repurposing
- Unlocks faster innovation in treatment development
DIGITAL TWINS
Digital Twins of patients and diseases
Our proprietary AI algorithms create digital twins—virtual representations of both patients and diseases. These models reflect the underlying biology at the single-cell, gene-network, and tissue level, allowing us to:
- Match patients to molecularly similar subgroups
- Simulate treatment response in silico
- Predict optimal therapies with unprecedented accuracy
FUNCTIONALITY
How it works:
Mavatar leverages single-cell RNA sequencing, AI algorithms, and molecular datasets to create medical avatars—digital twins of both diseases and patients. These avatars guide treatment decisions by matching patient biology with targeted therapies through large-scale simulation and network-based prediction.
Mavatar integrates millions of publicly available transcriptomic and proteomic datasets. These include patients from various ethnicities, geographies, and disease stages. By using this open, global dataset:
We capture diverse, real-world biological variation
We avoid biases or delays related to new patient recruitment
We learn across diseases, treatments, and populations
Our multi-layer qualification process ensures only high-quality molecular data is used in building disease models. The richness and diversity of the data mean that trends like immunity shifts, pandemics, or emerging biomarkers are built into the system from the start.
Using DINA, we analyze tens of thousands of patient samples to model biological processes down to the single-cell and gene-network level. This results in structured disease subtypes built on molecular similarity. These models:
Reflect underlying gene and protein activity
Identify key disease-driving pathways and cell interactions
Scale across tissue types, diseases, and conditions
Our framework is disease-agnostic and supports the modeling of any condition with sufficient molecular data.
Each patient is matched with a pre-computed medical avatar—a digital twin built on molecular similarity to thousands of others. Once matched:
Simulations are run to test treatment options
AI ranks drugs by their predicted biological impact
Physicians receive therapy recommendations based on a relative scoring system
This replaces trial-and-error with predictive modeling and speeds up time to optimal treatment.
One of DINA’s greatest strengths is cross-disease learning. By integrating data across related conditions and tissues (e.g., lungs affected by cancer, asthma, COVID), Mavatar uncovers:
Shared and unique biological mechanisms
Repurposing opportunities for treatments
Broader biological insights that accelerate both research and clinical outcomes
DINA learns not just from human patients but from animal models, cell lines, and clinical trial data—enabling deep, layered biological discovery.
With the help of graph theory and network analysis, Mavatar's AI prioritizes therapies based on how central their targets are in a patient’s disease network. This includes:
Identifying key gene interactions and disrupted pathways
Ranking therapies based on predicted efficacy and mechanism relevance
Supporting personalized off-label or multi-target recommendations
Each treatment is simulated in silico to find the best path forward—maximizing effectiveness while minimizing unnecessary side effects.
Why it Matters
• No guesswork: Our AI doesn't rely on assumptions—it learns from real biological data.
• No limits: Our technology can scale across any well-researched disease.
• No delays: Our digital infrastructure allows fast, remote analysis and delivery.
We don’t use generative AI—we use machine learning and graph-based algorithms built for accuracy, explainability, and clinical relevance.
A FUTURE FOR EVERYONE
Built for the future of medicine
From personalized cancer treatment to global drug development, Mavatar's technology represents a fundamental shift—from trial-and-error to data-driven precision. We are not just building digital twins. We’re building the foundation for a future where every patient has one.