SYNTHETIC COGNITION FOR BIOHEALTH DATA

Predict Outcomes. Explain Biology. Simultaneously.

Avatar Cognition introduces a new unified paradigm for biomedicine: Synthetic Cognition. Our disruptive AI technology doesn't just predict outcomes; it explains the biology behind them and enables the seamless extraction of valuable knowledge. Synthetic Cognition bridges the gap between translational medicine cohort multi-omic datasets and actionable clinical knowledge.

Sophisticated Multi-Omics. Zero Ad-Hoc Engineering.

Unlike the rest of AI technologies, Synthetic Cognition is a scalable General-Purpose Product. It can handle heterogeneous data by-nature (e.g., merging microbiome counts with clinical metadata) without manual parameter tuning or data engineering. Synthetic Cognition can learn from very small amounts of data (e.g., a very small bulk multiomics cohort) dramatically reducing the "Cold Start" problem and unbalanced cohorts that stall traditional Foundation Models, delivering results in hours, not months.

Validated technology. SOTA transparency and results.

Synthetic Cognition has proven its unparalleled versatility and results across a wide spectrum of bio-health use cases —from medical imaging features to molecular biology.

Automated Multi-Omic Discovery | Partner: Leading Research Institute in Immunology

• The Challenge: Extracting meaningful signals from a small, high-value cohort (n=10) by integrating three distinct layers: Transcriptomics, Methylomics, and the Microbiome

• The Diversity: The system automatically harmonized these heterogeneous data types without ad-hoc engineering.

• The Outcome: The model successfully identified cross-omic signatures (e.g., methylation sites regulating gene expression) that distinguished viral controllers from non-controllers.

Simultaneous Mechanism Discovery | Partner: Clinical-Stage Biotech Company

• The Challenge: Understanding therapeutic vaccine efficacy in a complex Phase 1 trial.

• The Diversity: Focused on longitudinal transcriptomics (time-series gene expression across 5 timepoints).

• The Outcome: Simultaneously predicted responders and discovered "new cellular mechanisms" involved in antiviral defense, extending findings beyond the customer's standard analysis.

Non-Invasive Prediction (Radiomics) | Partner: Reference Center for Personalized Oncology

• The Challenge: Predicting immunotherapy response in solid tumors using aprox. 100 radiomic features extracted from CT images, with strict data privacy constraints.

• The Diversity: Focused on Radiomics (quantitative feature-based data).

• The Outcome: Deployed on-site in one morning, the model identified a radiomic signature associated with response while revealing hidden biases in the data gathering process.

Rare Event Prediction (Clinical Data) | Partner: Major Pediatric Hospital

• The Challenge: Predicting sepsis in a pediatric dataset where only 2% of cases were positive

• The Diversity: Focused on Clinical Metadata and highly unbalanced classes.

• The Outcome: Achieved 2nd place in a global benchmark (AUC 0.592), outperforming XGBoost models without needing synthetic data generation.

Heterogeneous Data Integration | Partner: Leading Microbial Genomics Group

• The Challenge: Linking gut microbiota to viral infection and lifestyle factors.

• The Diversity: Focused on Heterogeneous Data (merging clinical, dietary, and blood variables with bacterial counts).

• The Outcome: Integrated 274 diverse variables to discover 5 new bacterial correlations previously unknown to the researchers.

Beating the Giants in DNA Sequencing | Public SOTA benchmark

We benchmarked Synthetic Cognition against leading Foundation Models (NVIDIA-sponsored NT-v2, DNABERT-2) on a wide array of genomic tasks.

• Superior Accuracy: Outperformed Transformers in 52% of DNA sequencing tasks, achieving up to ~20% improvement over the best-performing models in specific tasks.

• Extreme Efficiency: Achieved these results using less than a millionth of the training data required by Foundation Models (e.g., 0.00017% training needs vs. massive pre-training).

State-of-the-Art Multi-Omic Classification | Public SOTA benchmark

We validated our engine against the Multi-View Graph Neural Network (MVGNN) on a complex Breast Cancer dataset.

• The Result: Achieved 0.934 Accuracy in subtype classification (Luminal A/B, HER2, Triple-Neg), surpassing the MVGNN score of 0.918.

• The "White Box" Edge: Unlike the Neural Network, our model provided full explainability of why specific subtypes were classified.

Unlock the Full Potential of Your Cohort Data.

Don't choose between prediction and understanding. Get both.
Deploy a scalable, general-purpose engine that turns data into simultaneous insight and foresight.
Contact Us at sales@avatarcognition.com

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