Genomic Analysis

Genomic sequencing and multiomics data are processed with GPU-accelerated AI to improve variant detection speed and accuracy.

Business impact

• Speed of genomic analysis — Parallel GPU processing reduces time required for genome alignment and variant calling
• Accuracy of variant detection — AI-driven multiomics integration improves precision in identifying genetic variants
• Cost per genomic analysis — Automation and scalable cloud workflows lower unit costs for large-scale studies

Data requirements

• Genomic sequencing data (Text) — Primary input for variant calling and genome alignment processes
• Multiomics datasets (Structured) — Integrate transcriptomics, proteomics, and metabolomics for comprehensive analysis
• Clinical health records (Structured) — Provide phenotypic context to link genetic variants with patient outcomes

AI methods and techniques

• Predictive AI — Used to identify disease-associated variants and predict patient risk profiles
• Generative AI — Supports simulation of protein structures and variant effects for drug target discovery

AI models and model families

GPT-4o, Claude, Llama, AlphaFold, Vertex AI

Industries

Real-world evidence

6 documented case studies on record.

Companies using this: BioAro, Dante Omics AI, Galatea Bio, Illumina, National Health Research Institutes (NHRI), Xponent L.