Research
Provotics reads cancer's molecular signature from gene expression. The work is about doing that accurately, calibrating the uncertainty honestly, and keeping every call traceable to biology.
Each tumor carries roughly 18,000 gene measurements. Provotics learns the patterns that distinguish tissues of origin and molecular states from that full expression profile.
Multiple models score each profile and are combined, which is more robust than any single classifier on noisy, correlated, high-dimensional data.
Probabilities are temperature-scaled so a stated 80% means roughly 80% in practice, the difference between a number you can act on and one you can't.
Out-of-distribution profiles are flagged and abstained on rather than forced into a label, so a wrong call is caught instead of reported.
Numbers from held-out tumors and an independent cohort, not the training set.
Fully independent, out-of-distribution cohorts show the generalization gap that honest external validation always reveals. Some rare sites remain weak, and the model is deliberately built to abstain on samples it doesn't recognize rather than guess.
Provotics is a research and educational project. It is not a medical device, and its outputs are research hypotheses, not clinical decisions. Read more on the Safety page.
Open to research-lab and biotech collaborations. Request access to evaluate the model under our access agreement.
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