No single omics layer tells the full biological story. This post examines how AI integrates genomics, transcriptomics, proteomics, metabolomics, and spatial data into clinically useful models—and where the field still …
A new AI model called PARM can predict and design gene promoter activity from DNA sequence alone—opening the door to programmable gene expression for therapy and regenerative medicine.
The final post in our Agentic Omics series synthesises two years of progress and charts the path forward: near-term breakthroughs, medium-term transformations, and the enduring challenges that will define biological AI …
The ultimate vision of agentic omics is the self-driving laboratory: AI agents that design experiments, control robotic platforms, analyse results, and iterate — with humans providing goals and oversight.
AI models that can design proteins, predict pathogen evolution, or generate novel biological sequences raise real biosecurity concerns. This post takes an honest look at the dual-use risks and governance frameworks.
The tension between open science and commercial interests is acute in biological AI. AlphaFold 2 was open; AlphaFold 3's code was initially restricted. ESM is open; many pharma AI tools are not. This matters for …
AI models trained predominantly on European-ancestry genomes perpetuate health disparities. This post examines the ethical dimensions of omics AI — from data representation to algorithmic fairness to equitable access.
The ultimate test of omics AI is clinical impact. This post examines the gap between computational results and clinical utility, FDA regulatory pathways, and real-world evidence of AI tools changing patient outcomes.
Complex biological questions require multiple AI perspectives. Multi-agent systems — where specialized agents debate, collaborate, and peer-review each other — may be the path to more reliable biological reasoning.
Cancer is arguably the disease where multi-omics AI integration has the most clinical impact today. This post examines how agentic systems could transform the precision oncology workflow.
