Federated Multi-Omic Discovery Digital Worker
This digital worker coordinates AI agents that generate data-driven hypotheses, assemble federated patient cohorts across institutions, integrate multi-omic data (genomics, transcriptomics, proteomics, imaging), perform rigorous statistical validation, discover novel biomarkers, prioritize therapeutic targets, and generate publication-ready discovery reports..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
AI hypothesis generation interface displaying 15 ranked hypotheses for multi-omic biomarker discovery with feasibility and impact scores
Federated cohort assembly across global healthcare institutions with multi-omic data availability matrix showing 53,847 patients
Multi-omic integration dashboard with AI agents performing federated statistical analysis and hypothesis testing in real-time
Discovery report and publication package with validated 3-gene signature biomarker and comprehensive scientific documentation
AI Agents
Specialized autonomous agents working in coordination
Hypothesis Generator Agent
Formulating testable scientific hypotheses requires deep domain expertise and comprehensive literature review, limiting the scope of discovery to pre-existing human knowledge.
Core Logic
Analyzes trial outcome data, patient characteristics, and treatment responses to automatically generate ranked scientific hypotheses. Considers biological mechanisms, literature support, data requirements, feasibility, and expected impact. Produces 10-15 prioritized hypotheses with rationale and required data modalities for each.
Multi-Omic Integrator Agent
Integrating data across different omic platforms (WGS, RNA-seq, mass spectrometry, imaging) requires complex normalization and harmonization that introduces batch effects if done incorrectly.
Core Logic
Orchestrates federated cohort assembly across healthcare institutions, harmonizes multi-omic data from different platforms and assays, performs quality control and batch effect correction, and produces integrated analysis-ready datasets with documented provenance for each data modality.
Statistical Validation Agent
Rigorous statistical validation of biomarker candidates requires proper cross-validation design, multiple testing correction, and replication across independent cohorts.
Core Logic
Executes hypothesis testing with appropriate statistical methods (t-tests, survival analysis, random forest). Performs cross-cohort validation, calculates effect sizes with confidence intervals, applies multiple testing correction, and generates forest plots and meta-analysis results documenting replication across 3+ independent cohorts.
Literature Context Agent
Placing discovery findings in scientific context requires comprehensive literature review spanning genomics, immunology, oncology, and drug development publications.
Core Logic
Continuously monitors and analyzes scientific literature relevant to discovered biomarkers and targets. Identifies prior publications, conflicting evidence, related discoveries, and citation-ready references. Provides competitive landscape assessment and novelty evaluation for patent applications.
Biomarker Discovery Agent
Identifying clinically useful biomarkers requires not just statistical association but validation of sensitivity, specificity, clinical utility, and assay feasibility.
Core Logic
Evaluates candidate biomarkers for clinical utility including ROC analysis (AUC, sensitivity, specificity), positive/negative predictive values, number needed to screen, subgroup performance validation, assay feasibility assessment, and regulatory pathway evaluation. Ranks biomarkers by combined clinical and commercial potential.
Target Prioritization Agent
Translating discovery findings into drug development programs requires assessment of druggability, existing programs, IP landscape, and commercial viability.
Core Logic
Evaluates therapeutic targets for druggability (protein structure, binding sites, modality options), assesses existing drug programs and licensing opportunities, analyzes IP landscape and freedom to operate, projects success probability based on target class, and estimates commercial potential including market size and peak sales projections.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
7 technologies
Architecture Diagram
System flow visualization