The objective of the study was to evaluate the use of AI to build multi-omic machine learning (ML) models and test if these models can learn associations between the datasets and ovarian cancer patient short- and long-term survival.
'This study incorporated one of the largest sets of multi-omic data from 235 ovarian cancer patients, to identify the key features from these datasets driving overall survival endpoints. It was no small feat to bring together these many complex datasets to create an AI-driven predictor of survival in ovarian cancer,' said
'The power of our technology is promising because our multi-omic machine learning models have the potential to overcome the gap defining the prognostic subgroups within ovarian cancer,' explained
'Ultimately, these models could support the tailoring of therapies to individual patients with the goal of positively affecting the overall survival of ovarian cancer patients,' said
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