公开(公告)号：US20140309122A1

公开(公告)日：2014-10-16

申请号：US14243920

申请日：2014-04-03

Applicant: NEC Laboratories America, Inc.

Inventor： Renqiang Min ,  Yanjun Qi ,  Salim Akhter Chowdhury

IPC: G06F19/24 ,  G06N99/00

CPC classification number: G16B40/00 ,  G06N20/00 ,  G16B5/00 ,  G16B50/00

Abstract: 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.

Abstract translation: 公开了知识驱动的稀疏学习识别可解释的高阶特征交互的系统和方法。 这是通过从基因特征和基因和蛋白质相互作用分组产生一个或多个官能团来实现的; 选择信息性基因和功能相互作用，显示目标疾病的差异模式并产生减少的特征空间; 并通过检查所有可能的交互特征对（以及可能的高阶特征相互作用）来查找减少的特征空间，从而识别标记的组合和特征相互作用的复杂模式，以便在稀疏学习框架中提供关于表型的信息，以选择信息 相互作用和基因。