Our Approach
to Innovation
We combine real-world clinical data, whole-transcriptome sequencing, and machine learning to address specific unmet needs that deliver better clinical decisions in gynecologic cancers.
Real-world clinical cohorts
Our clinical cohorts are built to reflect diversity across patient ethnicities, cancer diagnoses and stages, and the benign conditions that commonly present with symptoms and will ultimately be eligible for our test.
RNA-based machine learning for classifier development
We use whole-transcriptome sequencing, novel feature engineering and machine learning to identify meaningful molecular patterns that separate diseased states from normal – leading to classifiers that inform better clinical decisions.
Generate new clinical insights
Our highly curated biorepositories in gynecologic cancer support both current product development and inform future unmet clinical needs that we can address.