The Agentic Omics Vision: LLMs Meet Domain-Specific AI
The central thesis of this series: Agentic Omics combines LLM reasoning with domain-specific biological AI models to create autonomous systems capable of end-to-end biological discovery.
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Agentic AI
What Is Agentic AI? From Chatbots to Autonomous Scientific Agents
Agentic AI is not just LLMs answering questions — it's AI systems that autonomously plan, reason, use tools, and execute multi-step scientific workflows. This post defines the paradigm and surveys its emergence in …
Single-Cell Multi-Omics: The Cellular Resolution Revolution
Single-cell technologies now measure multiple omics layers simultaneously in individual cells. AI models for single-cell multi-omics are enabling unprecedented resolution in understanding cellular heterogeneity, …
AI for Metagenomics: Decoding the Microbiome
How deep learning and transformer models are transforming metagenomics, from assembly and taxonomic classification to predicting microbiome-disease associations and antibiotic resistance.
AI for Proteomics: From AlphaFold to Protein Design
Protein AI is the most mature omics AI field. But structure prediction was just the beginning—the frontier is now protein design, function prediction, and interaction modeling.
Benchmarks and Evaluation: How Do We Know If Omics AI Actually Works?
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 Data Infrastructure Challenge: From Raw Reads to AI-Ready Datasets
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.
Foundation Models Meet Biology: The Transformer Revolution in Life Sciences
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.
