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

• Main Authors: MIN RENQIANG, CHOWDHURY SALIM AKHTER, QI YANJUN
• Format: Patent
• Language: English
• Published: 16.10.2014
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; PHYSICS

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.