公开(公告)号：US10192642B2

公开(公告)日：2019-01-29

申请号：US15150639

申请日：2016-05-10

Applicant: Macau University of Science and Technology

Inventor： Yong Liang ,  Hua Chai ,  Xiao-Ying Liu

IPC: G06F19/00 ,  G06F19/12 ,  G16H50/50

Abstract: A system and a method for determining an association of at least one biological feature with a medical condition, in particularly, but not exclusively, a system and a method of determining an association of at least one biological feature in form of a gene expression with cancer or a subtype of cancer that can include the generation of a simplified protein-protein interaction network based on processed biological data. The system and respective method is especially suitable for analysis of high dimensional and low sample size biological datasets such as in cancer research.

公开(公告)号：US20170329926A1

公开(公告)日：2017-11-16

申请号：US15150639

申请日：2016-05-10

Applicant: Macau University of Science and Technology

Inventor： Yong Liang ,  Hua Chai ,  Xiao-Ying Liu

IPC: G06F19/00 ,  G06F19/12

CPC classification number: G16H50/50 ,  G06F19/00 ,  G06F19/12

Abstract: A system and a method for determining an association of at least one biological feature with a medical condition, in particularly, but not exclusively, a system and a method of determining an association of at least one biological feature in form of a gene expression with cancer or a subtype of cancer that can include the generation of a simplified protein-protein interaction network based on processed biological data. The system and respective method is especially suitable for analysis of high dimensional and low sample size biological datasets such as in cancer research.

公开(公告)号：US20170329925A1

公开(公告)日：2017-11-16

申请号：US15150580

申请日：2016-05-10

Applicant: Macau University of Science and Technology

Inventor： Yong Liang ,  Hua Chai ,  Zi-yi Yang ,  Xiao-Ying Liu

IPC: G06F19/00 ,  G06F17/18

CPC classification number: G16H50/50 ,  Y02A90/26

Abstract: A system and a method for determining an estimated survival time of a subject with a medical condition utilizes the novel RS-AFT model and is especially suitable and highly advantageous for survival analysis based on microarray gene expression data because of its exceptional performance of gene selection, stable noise resistance, and high prediction precision.