公开(公告)号：US10438699B2

公开(公告)日：2019-10-08

申请号：US15150608

申请日：2016-05-10

Applicant: Macau University of Science and Technology

Inventor： Yong Liang ,  Hai-Hui Huang ,  Xiao-Ying Liu

IPC: G06N7/02 ,  G16H50/20 ,  G06N5/02

Abstract: A system and a method for determining an association of one or more biological features with a medical condition provides empirical results and simulations confirming that the involvement of both L1/2-regularized logistic regression and L2-regularized logistic regression in the regression model is highly competitive against usual approaches like Lasso, L1/2, SCAD-L2, and Elastic net in analyzing high dimensional and low sample sizes data.

公开(公告)号：US10515724B2

公开(公告)日：2019-12-24

申请号：US15150732

申请日：2016-05-10

Applicant: Macau University of Science and Technology

Inventor： Yong Liang ,  Hai-Hui Huang ,  Xiao-Ying Liu

IPC: G06N7/02 ,  G16H50/20

Abstract: A method and a system for determining an association of at least one biological feature with a medical condition utilizes the novel L1/2 penalized network-constraint regression model to achieve an improved biological analysis, in particular by solving high-dimensional problems. The method and the system of the present invention attain high accuracy and preciseness.

公开(公告)号：US20170329913A1

公开(公告)日：2017-11-16

申请号：US15150732

申请日：2016-05-10

Applicant: Macau University of Science and Technology

Inventor： Yong Liang ,  Hai-Hui Huang ,  Xiao-Ying Liu

IPC: G06F19/00 ,  G06N7/00

Abstract: A method and a system for determining an association of at least one biological feature with a medical condition utilizes the novel L1/2 penalized network-constraint regression model to achieve an improved biological analysis, in particular by solving high-dimensional problems. The method and the system of the present invention attain high accuracy and preciseness.

公开(公告)号：US20170329912A1

公开(公告)日：2017-11-16

申请号：US15150608

申请日：2016-05-10

Applicant: Macau University of Science and Technology

Inventor： Yong Liang ,  Hai-Hui Huang ,  Xiao-Ying Liu

IPC: G06F19/00 ,  G06N5/02

CPC classification number: G16H50/20 ,  G06N5/02 ,  G06N7/023 ,  G06N20/00

Abstract: A system and a method for determining an association of one or more biological features with a medical condition provides empirical results and simulations confirming that the involvement of both L1/2-regularized logistic regression and L2-regularized logistic regression in the regression model is highly competitive against usual approaches like Lasso, L1/2, SCAD-L2, and Elastic net in analyzing high dimensional and low sample sizes data.