Machine learning model for predicting therapeutic vulnerabilites in patient cancer tissue samples

Abstract

Machine learning models to predict, based on genomic data and gene expression data associated with a particular patient cancer tissue sample, one or more characteristics for that particular patient cancer tissue sample. The machine learning models are trained using respective feature sets representing one or more topological characteristics of respective protein‑protein interaction, “PPI”, networks representing models of different cancers, and that indicate reaction pathways associated with the protein interactions. [Figure 4]