Turn cancer mutation datainto evidence-rankeddrug repurposinghypotheses.
ONCOQ.TECH helps oncology R&D and translational research teams review de-identified mutation data, prioritise biologically relevant signals, connect them to pathway context, and generate evidence-traced hypotheses for expert review.
Oncology Evidence Workspace
Rank mutation signals, inspect pathway context, and prepare review-ready hypotheses.
Cohort files reviewed
3
De-identified demo datasets
Mutation signals ranked
44
Prioritised for research review
Genes requiring review
13
Linked to pathway context
Validation status
RUO
Research-use only
Prioritise
Score mutation signals by biological relevance, recurrence, and available evidence.
Evidence ranking in progress
Investigate
Review pathway links, known limitations, and supporting references before escalation.
Requires domain review
Brief
Export a structured research brief for internal review, partner discussion, or validation planning.
Ready for validation planning
Research-use only. ONCOQ.TECH supports research planning and expert review. Outputs are not clinical diagnoses, treatment recommendations, prescribing advice, or patient-level decisions. Downstream use requires qualified expert review, validated evidence sources, and appropriate governance.
Mutation interpretation in oncology research is slow, fragmented, and hard to translate into action.
Complex mutation data
Turning variant tables into defensible research direction takes manual review across many tools and references.
Slow, expensive drug repurposing
Mutation-to-pathway-to-compound hypotheses are missed because the workflow is fragmented.
No integrated workspace
Research teams lack a single place that connects mutation, risk modelling, and therapeutic hypothesis with evidence provenance.
Three IP-aligned modules in one research workflow.
Powered by UM deep-tech IP: a hybrid AI + quantum-inspired computational approach for mutation detection, cancer risk prediction, and drug repurposing.
Mutation detection & prioritisation
Prioritises mutation signals from genomic data using pathway and evidence context.
Cancer risk prediction
Estimates a research-use cancer risk category from mutation patterns. Not a clinical diagnosis.
Drug repurposing recommendation
Ranks existing drug candidates for repurposing investigation, tied to pathway evidence.
A structured review path from mutation upload to validation-ready hypothesis.
Built to reduce manual triage, preserve evidence provenance, and make every candidate easier to defend in internal review.
Upload mutation data
Import a de-identified cohort mutation table or demo dataset.
Rank mutation signals
Prioritise variants and genes using relevance scoring and evidence availability.
Review biological context
Inspect pathway links, supporting evidence, limitations, and confidence boundaries.
Generate research brief
Create a structured output for R&D review, partner discussion, or validation planning.
Built for teams who need defensible oncology hypotheses, not another dashboard.
Hospitals & precision oncology units
Defensible mutation review for hospital research teams running de-identified panels under research-use governance.
Molecular diagnostic labs
Structured output for labs that need to share ranked mutation signals and pathway context with R&D collaborators.
Pharma / biotech R&D
Early hypothesis-class signal for drug-repurposing teams, tied to mutation evidence and pathway rationale.
Research commercialisation partners
University commercialisation, translational research offices, and IP-aligned partners running structured pilots.
Every hypothesis must explain why it deserves review.
ONCOQ.TECH keeps the mutation, pathway, evidence category, limitation, and next validation step connected so reviewers can see the rationale behind each candidate.
Mutation relevance score
Shows why a mutation or gene was prioritised for review.
Pathway context
Links the signal to biological mechanisms and affected pathways.
Evidence category
Separates literature support, database evidence, computational inference, and internal assumptions.
Known limitation
Flags weak evidence, missing validation, cohort bias, or unresolved biological uncertainty.
Next validation step
Suggests what reviewers should check before advancing the hypothesis.
Source provenance
Keeps references and data origins attached to the candidate output.
A research-use product with multiple aligned revenue lines.
B2B research SaaS
Per-team subscription for hospital and lab research workspaces.
Hospital / lab pilot package
Fixed-fee pilot with onboarding, validation support, and review packets.
Research partnership model
Co-developed research projects with UM, hospitals, and aligned labs.
API licensing for pharma
Structured mutation-to-pathway signal licensed to pharma and diagnostic platforms.
See how a mutation table becomes a review-ready oncology hypothesis.
Explore a demo cohort, review ranked mutation signals, inspect pathway evidence, and generate a research-use brief with limitations clearly stated.