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Case Study

Predictive modeling to identify mechanical biomarkers to accelerate oncology diagnosis

Machine learning to maximize signal from noise to differentiate normal vs malignant tissue


Our client has a technology that can differentiate cell types in patient biopsies. This innovation promises to accelerate the diagnosis of cancer and offer personalized treatment recommendations. Our client needed data science driven analysis of the machine signal to identify mechanical biomarkers to differentiate normal and malignant tissue.


We feature engineered the machine signal and applied clustering, regression and machine learning analysis. We built a data engineering pipeline to handle large scale measurement data. We trained a predictive model to differentiate between normal and malignant cells, providing insight into the most important mechanical biomarkers.

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