Explainable AI for life science

The importance of "the why"Real problem-solving requires more than accurate predictions

Automatic outputs cannot be justified and reveal no new information. If you can’t understand "the why" behind a result, then you don’t really know anything at all.

Use cases

Explainability

Rational drug design

We identify non-trivial and explainable relationships in data. Our interpretable models elevate scientists’ understanding of underlying mechanisms, accelerating the drug discovery process.

Rational drug design, ASO tutorial

High performing

Simple is powerful

The QLattice outperforms traditional machine learning techniques, even on small data sets. Its straightforward models identify only what is both true and sufficient, making models easy-to-verify.

Simple is powerful, Benchmarking paper

While tools do exist to estimate feature importance, weight, and interactions, I don’t see any other method being as supportive as the QLattice in building stories about trends in data, especially linking insights from data to biology. Abzu is unique!

Head of Bioinformatics, ALK

Clear and powerful insights, even for small data sets

Close the gap between hypothesis-driven research and traditional machine learning techniques.

Drug discovery

Clinical trials

Bioinformatics

What are Q waiting for?

Extract insights and explain the real relationships in your data. It’s time for transparency.