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Castleman Disease: Does Machine Learning Hold Key to Better Treatment?

Combining large omic and clinical trial data sets with machine learning algorithms has allowed Medidata and CDCN to identify six new patient subgroups of rare Castleman disease. How can this discovery advance diagnostics and drug development for this rare disease? Allie Nawrat finds out.

After hearing a talk by Castleman Disease Collaborative Network co-founder Dr David Fajgenbaum as part of a lecture series about challenging topics in the life sciences, artificial intelligence firm Medidata decided to stay in touch with Fajgenbaum and his company.Medidata chief data officer David Lee explains: “One day we were talking [with Dr Fajgenbaum] about how far the [Rave Omics] product and machine learning has come, and he was telling us about how much data he has been able to collect on [Castleman disease].“Then as a proof of concept, we decided to work on this collaboration together.”Combining CDCN’s large data sets from patients suffering with this rare, inflammatory disease with Rave Omics’ machine learning capabilities has led to novel insights about the disease that help to understand which patients will respond best to existing treatments and identify new drug targets for patients responding less well. Continue reading