The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Apr. 01, 2025

Filed:

Dec. 03, 2018
Applicant:

Illumina, Inc., San Diego, CA (US);

Inventors:

Shile Zhang, San Diego, CA (US);

Mengchi Wang, La Jolla, CA (US);

Aaron Wise, San Diego, CA (US);

Han Kang, San Diego, CA (US);

Vitor Ferreira Onuchic, San Diego, CA (US);

Kristina Kruglyak, San Diego, CA (US);

Assignee:

Illumina, Inc., San Diego, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G16B 40/20 (2019.01); C12Q 1/6886 (2018.01); G06F 17/15 (2006.01); G06F 17/16 (2006.01); G06F 17/18 (2006.01); G16B 45/00 (2019.01); G16B 50/20 (2019.01); G16B 50/30 (2019.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01);
U.S. Cl.
CPC ...
G16B 40/20 (2019.02); C12Q 1/6886 (2013.01); G06F 17/153 (2013.01); G06F 17/16 (2013.01); G06F 17/18 (2013.01); G16B 45/00 (2019.02); G16B 50/20 (2019.02); G16B 50/30 (2019.02); G16H 50/20 (2018.01); G16H 50/50 (2018.01);
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.


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