Genomic Analysis

Use AI to accelerate and improve genomic data interpretation for healthcare innovation

Business impact

• Research productivity — Increases throughput by automating complex genomic data interpretation tasks
• Time to insight — Shortens duration from data acquisition to actionable genomic insights
• Prediction accuracy — Enhances reliability of variant function and disease association predictions
• Clinical decision turnaround — Speeds up genetic diagnosis and personalized treatment planning
• Grant success rate — Improves quality and timeliness of research outputs for funding applications

Data requirements

• DNA/RNA sequencing data (Text) — Primary genomic input for variant detection and analysis
• Epigenomic and ChIP-seq data (Image) — Provides regulatory context for non-coding DNA function
• Clinical records and phenotypic data (Structured) — Links genomic variants to patient outcomes and traits
• Scientific literature databases (Text) — Supports hypothesis generation and validation from prior knowledge
• Multiomics datasets (Numeric) — Integrates proteomics, transcriptomics for comprehensive analysis

AI methods and techniques

• Predictive AI — Predicts variant functions and disease associations from genomic data
• Generative AI — Generates hypotheses and experimental protocols for validation
• Agentic AI — Automates iterative analysis workflows and user interactions

AI models and model families

Gemini 3 Pro, GPT-4o, Adelie Python package, Random Forest, Deep Neural Networks

Industries

Real-world evidence

6 documented case studies on record.

Companies using this: Alpha Genome Research Assistant, Boston University Chobanian & Avedisian School Medicine, Dante Omics AI, Microsoft, Stanford University, Wellcome Sanger Institute.