ZetaOmics introduces an autonomous "Computational Biologist" persona (agent) that performs real, end-to-end bioinformatics and multi-omic analysis — across any cancer type, and is purpose-built for the rare and pediatric cancers that have long been underserved by dedicated computational resources and meaningful bioinformatics.

First introduced as part of the withZeta.ai development roadmap the Company unveiled on May 7, 2026, ZetaOmics now moves from roadmap to reality. It is being made available initially through an early-access program for a select group of leading academic and industry bioinformatics teams and Lantern Pharma collaborators, whose real-world use will guide refinement ahead of broader commercial availability. This phased rollout is designed to validate the module against the most demanding research workflows while establishing the reference relationships that seed a larger subscriber and partnership base.

The current generation of AI research tools forces a choice between a brilliant generalist that has read everything but can run nothing, and legacy pipelines that execute fixed routines but cannot reason. ZetaOmics is built as a third option: an agent that both reasons and runs — designing the analysis, executing it independently on real biological data, defending its methodological choices, and returning publication quality results to provide true real-time value to researchers.

Intelligence embedded in every tool & module — not just automation

Most emerging agentic bioinformatics systems wrap a language model around off-the-shelf software, automating command-line execution. That approach can still build a confounded cohort, choose the wrong statistical test, or compare datasets that were never comparable — returning a confident answer that only an expert would recognize as wrong. ZetaOmics takes the opposite approach: it operates on pre-computed, harmonized, multi-omic data layers with domain intelligence embedded directly into each of its fourteen tools. Where conventional systems will run a flawed analysis and hand back flawed results, ZetaOmics is designed to recognize the flaw, decline to run it, and explain how to fix the experimental design.

Every analysis executes on withZeta.ai’s production runtime with unified authentication and per-execution logging — who asked what, which tools ran, which data was accessed — producing a queryable, exportable audit trail suited to regulated, compliance-sensitive research.

A Defining Step for AI in Oncology

"For decades, the deepest bottleneck in drug discovery hasn’t been data; it has been judgment. The rare instinct of a great bioinformatician or computational cancer biologist—to know which test the data calls for, when two datasets should not be compared, and when a result is true signal rather than statistical noise—is scarce, costly, and often lost when that expert leaves. With ZetaOmics, we’ve worked to encode that judgment into an autonomous agent and make rigor the default — so that any researcher, anywhere, can run analysis at a level that was once reserved for the best-resourced labs in the world.

We believe this is what the next decade of oncology R&D will be built on: co-scientists that don’t just retrieve knowledge, but generate it — responsibly, reproducibly, and at a pace that finally matches the urgency facing patients. By opening ZetaOmics first to the world’s leading bioinformatics teams and our collaborators, we intend to prove its value where the science is hardest, and in doing so open entirely new markets, new partnership and collaboration opportunities, and new subscription revenue for Lantern — across every cancer, not only the rare ones. This is how we scale withZeta.ai into a durable, non-dilutive growth engine for our shareholders while also advancing our own pipeline," said Panna Sharma, President and Chief Executive Officer of Lantern Pharma and Founder of withZeta.ai.

Judgment and automation in action

The difference between orchestrating tools and encoding judgment is easiest to see in practice. In each example below, a single natural-language request triggers a chain of expert decisions that a skilled bioinformatician would make by hand over days or weeks — executed autonomously, with the reasoning made explicit and auditable.

1 · Finding a drug vulnerability in a rare, hard-to-treat cancer

The ask: "Are there drug vulnerabilities in KRAS-mutant lung cancer, and are they real at the protein level?"

What ZetaOmics does autonomously:

  • Builds batch-safe cohorts, checking for mixed pipelines and confounder risk before any comparison runs.
  • Enforces functional consequence, so only true KRAS loss-of-function variants define the mutant group and benign missense noise doesn’t dilute the signal.
  • Checks data coverage and power, confirming enough mutant and wild-type lines carry drug and CRISPR data, and reporting effect sizes, confidence intervals, and FDR — not just p-values.
  • Validates hits at the protein level via RNA-protein concordance, distinguishing a druggable membrane target from a transcript with no protein behind it.

The impact: A vulnerability that a specialist team might spend weeks qualifying — and could easily over-claim — is surfaced, statistically defended, and protein-validated in a single conversational session.

2 · Refusing a comparison that would have produced a false discovery

The ask: "Compare gene expression between these two glioblastoma cohorts I’ve assembled."

What ZetaOmics does autonomously:

  • Profiles the cohorts first, detecting that they span multiple independent projects and mix incompatible quantification pipelines (RSEM vs. Kallisto).
  • Declines to run the naive comparison, because the batch difference would dominate the biology and generate a confident but false result.
  • Explains the confounder and fixes the design, auto-matching pipeline-consistent, anatomically correct controls so a valid contrast can run.

The impact: The single largest source of false discoveries in multi-cohort studies is caught and corrected automatically — the kind of quiet, judgment-based save that normally depends on a senior analyst noticing.

3 · Prioritizing a druggable biomarker in a pediatric cancer with sparse data

The ask: "What are the most promising druggable targets in this rare pediatric tumor?"

What ZetaOmics does autonomously:

  • Assembles a multi-evidence target shortlist — unfavorable-prognostic, tumor-restricted, secreted or membrane-accessible — checked against normal-tissue baselines.
  • Recognizes the data gap: with no validated prognostic panel available for this rare indication, it automatically pivots to a plasma-led path.
  • Surfaces circulating biomarker candidates — proteins measurably elevated in patient plasma versus healthy controls — as the actionable shortlist.

The impact: For exactly the populations the field tends to abandon, the agent adapts its own method to the available evidence rather than returning nothing — turning data scarcity into a defensible starting point.