K-PARTITE GRAPH BASED FORMALISM FOR CHARACTERIZATION OF COMPLEX PHENOTYPES IN CLINICAL DATA ANALYSES AND DISEASE OUTCOME PROGNOSIS

摘要

Systems and methods are disclosed that can analyze relationships between parameters in data matrices (e.g., collections of individual profiles). A graph topology can be defined on a data matrix with partitions as variables and vertices in all partitions and their potentials and edges as the co-occurrence of a pair of variable values in a profile. Individual graphs can be constructed from data and value co-occurrences for every profile, and a study data graph made as a union of all individual graphs. Heterogeneity Landmarks (HLs) can be determined from the study data graph, and graph-graph distances between individual graphs and all HLs. These distances can be used for prognoses based on similarity of a profile to one or more HLs.