The AI Oncology Research Intelligence agent — tumour biology to bedside to real-world.
A scientific research agent (not a chatbot, not a diagnostic tool) that discovers, connects, analyses, challenges and synthesises oncology knowledge from authorized literature, databases, datasets and enterprise tools — to help you understand what is known, what is unknown, what may be true, what has failed and what to test next.
Twelve deeply-connected knowledge domains spanning the research lifecycle.
Solid tumours & haematologic malignancies — breast, lung, colorectal, prostate, pancreatic, melanoma, GBM, leukaemias, lymphomas & rare cancers, plus premalignant and metastatic disease.
Oncogenes & tumour suppressors, driver mutations, the hallmarks of cancer, tumour microenvironment, angiogenesis, metabolism, clonal evolution, and mechanisms of metastasis.
Somatic & germline genomics, mutational signatures, bulk & single-cell/spatial transcriptomics, epigenomics, proteomics, metabolomics and the immune repertoire — connected across scales.
Checkpoint biology (PD-1/PD-L1, CTLA-4), CAR-T & cell therapies, bispecifics, cancer vaccines, TMB/MSI, neoantigens and mechanisms of immune escape & irAEs.
Genetic, functional-genomic (CRISPR), human, animal and multi-omics evidence for oncology targets — with tractability, synthetic lethality, safety, prior failures and research gaps.
Small molecules, mAbs, ADCs, protein degraders, radioligands, RNA and cell therapies — across the discovery-to-development cascade, including combinations and resistance-breaking strategies.
Diagnostic, prognostic, predictive and response biomarkers across tissue, liquid biopsy (ctDNA), imaging and digital pathology — with performance, validation and clinical-utility context.
Design, mechanism, population, endpoints (ORR, PFS, OS), biomarkers, comparators, results and toxicity — surfacing failed hypotheses, under-studied populations and design opportunities.
Acquired & intrinsic resistance mechanisms, on/off-target toxicity, cardio-oncology, immune-related adverse events and strategies to anticipate and mitigate them.
Differential expression, survival, mutational-signature, clonal-evolution, pathway/network and causal analyses, plus predictive modelling on authorized oncology datasets.
Reasons across Cancer ↔ Gene ↔ Variant ↔ Pathway ↔ Cell ↔ Biomarker ↔ Target ↔ Drug ↔ Trial ↔ Outcome — every claim traceable to primary literature, datasets or trial IDs.
Finds conflicting findings, unexplained resistance, missing biomarkers, poorly-validated targets and reproducibility gaps — then frames testable hypotheses and the experiments to resolve them.
Surface novel, testable research directions and drug-repurposing leads across the whole ecosystem — not a single paper.
Integrate literature, omics, trials and real-world data into one traceable evidence graph so hidden connections become visible.
A built-in scientific critic tests for confounding, batch effects, power, publication bias, replication and prior failures.
Research questions, proposals, protocols, SAPs, reviews and manuscript scaffolds — never fabricating data, patients, stats or citations.
Statements are tagged so you always know their standing — and confidence is stated, never inflated. Citations, DOIs, PMIDs, dataset and trial IDs are drawn from primary sources; if a source can't be verified it says so. Oncology Genius is a research-intelligence system, not a substitute for a physician — it does not diagnose or treat individuals.
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