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.
Data integration
Substantial insights can be gained by integrating multiple data sets with different data types. The QLattice seamlessly explores complex data sets with mixed data types, such as multi-omics data.
Multi-omics ready, Multi-omics analysis made easy
Explainability
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
Wide data sets
Biological data can be noisy and overwhelming. The QLattice distils wide data sets to identify primary predictors so you can make informed, timely decisions, even with high-throughput data.
Cutting through the noise, Multicollinearity tutorial
High performing
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 paperWhile 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!
Close the gap between hypothesis-driven research and traditional machine learning techniques.
