公开(公告)号：US20160259887A1

公开(公告)日：2016-09-08

申请号：US15049983

申请日：2016-02-22

Applicant: NEC Laboratories America, Inc.

Inventor： Renqiang Min ,  Sanjay Purushotham

IPC: G06F19/24 ,  G06N99/00

CPC classification number: G16B40/00

Abstract: An optimization-driven sparse learning framework is disclosed to identify discriminative system components among system input features that are essential for system output prediction. In biomarker discovery, to handle the combinatorial interactions among gene or protein expression measurements for identifying interaction complexes and disease biomarkers, the system uses both single input features and high-order input feature interactions.

Abstract translation: 公开了优化驱动的稀疏学习框架，以识别对系统输出预测至关重要的系统输入特征之间的区分系统组件。 在生物标志物发现中，为了处理识别相互作用复合物和疾病生物标志物的基因或蛋白质表达测量之间的组合相互作用，系统使用单一输入特征和高阶输入特征相互作用。