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 produces evidence-ranked mutation signals and drug-repurposing hypotheses for investigation. Outputs are not clinical recommendations and must be reviewed by qualified experts before any validation or downstream use.
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.
Bioinformatics leads
Reduce manual mutation triage and give each ranked signal a clear evidence trail, pathway context, and review status.
Translational research teams
Move from raw mutation lists to testable drug-repurposing hypotheses that can be discussed, challenged, and refined.
R&D operators
Standardise review packets across projects so internal teams can compare opportunities without losing provenance or assumptions.
Pharma partnering teams
Screen early research opportunities faster by reviewing mutation relevance, pathway fit, and repurposing rationale in one place.
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.
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.