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CASE STUDY

MultiClass Classification

Case Study part1
JADBIO produced two models, a Best Performing Model, with an AUC of .896 and a Best Interpretable model with an AUC of .679 in two minutes hands-on time and nine hours of analysis time. In addition to the models, the resulting feature selection and visualizations reveal key information about the individual cancer types and the expression data that differentiates their classification.
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Reading the molecular labels in cancer

Tissue origin and morphology have historically informed cancer diagnosis. Today molecular information offers the promise of more precise human cancer classification and treatment.

We uploaded expression data from 109 cancer samples with representation from 14 different tumor types onto the JADBIO platform to illustrate the role of automated machine learning in this endeavor.
Multiclass cancer diagnosis
Case Study part2
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