公开(公告)号：US20200176083A1

公开(公告)日：2020-06-04

申请号：US16208149

申请日：2018-12-03

Applicant: ILLUMINA, INC.

Inventor： Shile Zhang ,  Mengchi Wang ,  Aaron Wise ,  Han Kang ,  Vitor Ferreira Onuchic ,  Kristina Kruglyak

IPC: G16B40/20 ,  C12Q1/6886 ,  G16H50/20 ,  G16H50/50 ,  G16B45/00 ,  G16B50/30 ,  G16B50/20 ,  G06F17/15 ,  G06F17/16 ,  G06F17/18

Abstract: Provided is a computer-implemented method, including inputting to a trained machine learning classifier genomic information of a non-training subject that includes features from a tumor sample, wherein the trained machine learning classifier trained on features of tumor samples obtained from training subjects and their a responsiveness to checkpoint inhibition treatment and the machine-learning classifier is trained to predict responsiveness to the treatment, and generating a checkpoint inhibition responsiveness classification predictive of the subject's responding to the checkpoint inhibition with the trained machine-learning classifier, and reporting the checkpoint inhibition responsiveness classification using a graphical user interface. Also provided are a computer system for performing the method and a machine learning classifier trained by the method.

公开(公告)号：US20230245724A9

公开(公告)日：2023-08-03

申请号：US16208149

申请日：2018-12-03

Applicant: ILLUMINA, INC.

Inventor： Shile Zhang ,  Mengchi Wang ,  Aaron Wise ,  Han Kang ,  Vitor Ferreira Onuchic ,  Kristina Kruglyak

IPC: G16B40/20 ,  C12Q1/6886 ,  G16H50/20 ,  G16H50/50 ,  G16B45/00 ,  G16B50/30 ,  G16B50/20 ,  G06F17/15 ,  G06F17/16 ,  G06F17/18

CPC classification number: G16B40/20 ,  C12Q1/6886 ,  G16H50/20 ,  G16H50/50 ,  G06F17/18 ,  G16B50/30 ,  G16B50/20 ,  G06F17/153 ,  G06F17/16 ,  G16B45/00

Abstract: Provided is a computer-implemented method, including inputting to a trained machine learning classifier genomic information of a non-training subject that includes features from a tumor sample, wherein the trained machine learning classifier trained on features of tumor samples obtained from training subjects and their a responsiveness to checkpoint inhibition treatment and the machine-learning classifier is trained to predict responsiveness to the treatment, and generating a checkpoint inhibition responsiveness classification predictive of the subject's responding to the checkpoint inhibition with the trained machine-learning classifier, and reporting the checkpoint inhibition responsiveness classification using a graphical user interface. Also provided are a computer system for performing the method and a machine learning classifier trained by the method.