Early access for oncology research teams

Read Every Oncology Paper. Then Test What No One Has.

We use foundation models to analyze multi-omics data alongside the published literature, then generate pre-clinical hypotheses your lab can validate at the bench.

Request Early Access See the architecture

Built strictly for oncology research validation. Not a medical device and not for clinical decisions.

From the literature to the lab bench, in one workspace.

Three capabilities that turn a research question into a testable idea.

01

Literature Synthesis

Parse PubMed at scale and get cited summaries that keep contradictions visible instead of smoothing them over.

02

Molecular Pattern Analysis

Discover signals in gene expression data and chemical compound graphs that are hard to see one study at a time.

03

Hypothesis Generation

Rank candidate hypotheses by strength of evidence and prioritize them for wet-lab validation.

Platform architecture

Four stages, each one traceable back to its source data.

  1. STAGE 01

    Ingest

    Collect PubMed records, trial data, and multi-omics cohorts, normalized to standard gene, drug, and disease identifiers.

  2. STAGE 02

    Parse

    Extract entities, relationships, and claims from text, each linked to the passage that supports it.

  3. STAGE 03

    Model

    Deep learning models detect signals across expression profiles and chemical compound graphs.

  4. Powered by Claude STAGE 04

    Reason & Verify

    Frontier cognitive reasoning models, such as Claude (Anthropic), to ensure rigorous source citations. Candidates are then ranked for wet-lab validation.

Long-context reasoning for oncology manuals.

Treatment guidelines, trial protocols, and supplementary data run to hundreds of pages. With context windows of 200k+ tokens, we load whole documents in one pass instead of cutting them into disconnected chunks.

  • ✓Cross-references between sections stay intact.
  • ✓Every conclusion cites the page or section it came from.
  • ✓One call covers a full protocol and its amendments.
analyze_cohort.py 
# Illustrative example. SDK interface is in development.
import aicancer as ac

model = ac.OncologyLLM(
    reasoning_engine="claude-sonnet-5-5",
    context_window=200_000,
    require_citations=True,
)

cohort = ac.load_cohort(
    "tcga_brca",
    modalities=["rna_seq", "mutations"],
)

result = model.analyze_pathways(cohort, focus="PI3K/AKT", top_k=5)

for h in result.hypotheses:
    print(h.rank, h.statement, h.citations)

Infrastructure and compliance

Security is part of the architecture from the start.

HIPAA Architecture Ready

Designed to support HIPAA safeguards for protected data.

AES-256 Encryption

Encryption at rest, with TLS protecting data in transit.

Role-Based Access

Least-privilege permissions for every user and dataset.

Audit Logging

Every query and output is recorded for review.

These describe our architecture and design goals. They are not third-party certifications or audit attestations.

Working on cancer research? Get early access.

Tell us about your research area and the questions you want to answer. We read every message.

Request Early Access ceo@aicancerresearch.xyz