Claims of AI breakthroughs in biology are only as strong as their evaluation. This post critically examines benchmarks, metrics, and common pitfalls in evaluating biological AI models.
The bottleneck for AI in omics is often not the model but the data. This post covers the infrastructure, standards, and preprocessing pipelines that make omics data AI-ready.
Why transformer-style foundation models work so well on biological data, where the analogy to language breaks down, and which genomics, proteomics, and single-cell models matter most in 2026.
Before diving into AI, we need a clear map of what 'omics' actually covers. Each discipline studies a different layer of biological information, and AI readiness varies dramatically across them.
