KNOWLEDGE-DRIVEN SPARSE LEARNING APPROACH TO IDENTIFYING INTERPRETABLE HIGH-ORDER FEATURE INTERACTIONS FOR SYSTEM OUTPUT PREDICTION

摘要

Systems and methods are disclosed for Knowledge-Driven Sparse Learning to Identify Interpretable High-Order Feature Interactions. This is done by generating one or more functional groups from gene features and gene and protein interaction grouping; selecting informative genes and functional interactions that exhibit differential patterns for the target disease and to generate a reduced feature space; and searching exhaustively on the reduced feature space by examining all possible pairs of interacting features (and possibly higher-order feature interactions) to identify combination of markers and complex patterns of feature interactions that are informative about the phenotypes in a sparse learning framework to select informative interactions and genes.

主权项

1. A method for diagnosing a target disease using molecular signatures, comprising:
generating one or more functional groups from gene features and gene and protein interaction grouping; selecting informative genes and functional interactions that exhibit differential patterns for the target disease and to generate a reduced feature space; and searching exhaustively on the reduced feature space by examining all possible pairs of interacting features (and higher-order interactions if possible) to identify combination of markers and complex patterns of feature interactions that are informative about the phenotypes in a sparse learning framework to select informative interactions and genes.