Genomics

Introduction: The Precision Oncology Imperative Cancer is not one disease but hundreds—each with distinct molecular drivers, treatment responses, and clinical trajectories. The promise of precision oncology is simple in concept but staggering in execution: match the right treatment to the right patient at the right time, guided by the molecular profile of their tumor. In practice, this requires orchestrating a complex workflow: tumor sequencing to identify mutations, interpretation of those variants against clinical databases, integration of genomic data with transcriptomic and proteomic profiles, therapy matching against drug databases, clinical trial matching, and longitudinal monitoring for resistance and recurrence. Each step generates data, requires expert interpretation, and carries uncertainty. ...

The bottleneck for AI in computational biology is rarely a shortage of sophisticated models; it is the sheer difficulty of making biological data AI-ready. The “Agentic Omics” vision—where autonomous AI agents orchestrate domain-specific models to accelerate drug discovery—fundamentally rests on the assumption that these agents have access to standardized, clean, and computable data. In this post, we explore the unglamorous but critical foundation of omics AI: the data infrastructure. We trace the journey from raw sequencing reads to the structured tensor formats required by modern foundation models, exploring the evolving standards, the scale of the challenge, and how cloud infrastructure is adapting. ...

In the first post of this series, we mapped the omics landscape: genomics, transcriptomics, proteomics, metabolomics, metagenomics, phenomics. The next question is obvious: why did AI suddenly get so good at several of these fields at once? The short answer is that biology turned out to be unusually compatible with the same family of models that transformed natural language processing. DNA, RNA, proteins, and even single-cell expression matrices are not “language” in any literal sense, but they are structured symbol systems with long-range dependencies, rich context, and vast quantities of unlabeled data. That is exactly the setting where self-supervised foundation models thrive. ...