This technology is a set of algorithms for prediction optimization, which has applications for predicting customer behaviors, multiplex screening tests, predictive biomarkers, including cancer diagnostics.
Current machine learning algorithms can optimize multiplexed decisions. However, current solutions use underlying assumptions about interactions between factors to simplify computations. This leads to reduced performance due to a failure to find the most optimal solution since these assumptions are often not valid generally.
This technology describes a set of algorithms that use Bayesian learning to reduce the number of calculations to find optimal solutions without any inherent assumptions. Using real-world data these algorithms can be paired with accompanying algorithms to update the predictions and accuracy of these algorithms. While there is a range of applications for these algorithms, this technology has been validated as a tool for cancer screening when applied to external datasets. Additionally, these algorithms can be used to identify predictive biomarkers for therapeutics applications and predicting risks in general.
Patent Pending
IR CU23231
Licensing Contact: [Sara Gusik] (mailto:techtransfer@columbia.edu)
