Abstract: Computer-implemented processes for precision therapeutic biomarker screening for cancer include using a model-based overlapping clustering framework to assess large numbers of possible drugs and drug combinations against patient data, including cell line responsiveness. A multivariate regression model has been developed, along with a latent overlapping cluster indicator variable. The techniques employ a new finite mixture of multivariate regression (FMMR) model and expectation-maximization (EM) algorithm for modeling. The techniques can analyze large amounts of drug data and identify complex overlapping drug clusters, as well as cluster-wise drivers that facilitate identification of new drugs for treating pathologies, such as cancer, in patients.

Abstract: Computer-implemented processes for precision therapeutic biomarker screening for cancer include using a model-based overlapping clustering framework to assess large numbers of possible drugs and drug combinations against patient data, including cell line responsiveness. A multivariate regression model has been developed, along with a latent overlapping cluster indicator variable. The techniques employ a new finite mixture of multivariate regression (FMMR) model and expectation-maximization (EM) algorithm for modeling. The techniques can analyze large amounts of drug data and identify complex overlapping drug clusters, as well as cluster-wise drivers that facilitate identification of new drugs for treating pathologies, such as cancer, in patients.