If you have questions about our methods or findings, we welcome your feedback and inquiries.

t.ly/OA9fS

Thank you for your interest in our work! (8/n)

Our classifier is freely accessible on GitHub and can be applied to your datasets for KEAP1-NRF2 pathway analysis.
t.ly/S3JUv

When applied to #SRA data, the classifier detected constitutive pathway activation caused not only by known KEAP1-NRF2 alterations in cell lines, but also by previously unreported alterations on the system. (6/n)

The final model was cross-validated, achieving a
Specificity of 96.90%, discovering as well new alterations beyond the original scope of analysis within #TCGA. (5/n)

Our preliminary model helped reclassify ambiguous mutations, enriching our training data.
This iterative approach ensured diverse mutation representation, improving model robustness. (4/n)

We developed a naive Bayes-based classifier trained on #TCGA data to identify constitutive activation of the KEAP1-NRF2 pathway.
It focuses on mutations impacting pathway activity and leverages splicing junction signatures as biomarkers. (3/n)

The KEAP1-NRF2 pathway plays a critical role in cellular stress responses. Its constitutive activation is linked to genomic alterations, often found in cancer.
We explored a novel way to detect these activations using aberrant splicing junctions. (2/n)